Models

Response body for API keys.

Source code in src/zenml/models/v2/core/api_key.py

class APIKeyResponseBody(BaseDatedResponseBody):
    """Response body for API keys."""

    key: Optional[str] = Field(
        default=None,
        title="The API key. Only set immediately after creation or rotation.",
    )
    active: bool = Field(
        default=True,
        title="Whether the API key is active.",
    )
    service_account: "ServiceAccountResponse" = Field(
        title="The service account associated with this API key."
    )

`APIKeyResponseMetadata`

Bases: BaseResponseMetadata

Response metadata for API keys.

Source code in src/zenml/models/v2/core/api_key.py

class APIKeyResponseMetadata(BaseResponseMetadata):
    """Response metadata for API keys."""

    description: str = Field(
        default="",
        title="The description of the API Key.",
        max_length=TEXT_FIELD_MAX_LENGTH,
    )
    retain_period_minutes: int = Field(
        title="Number of minutes for which the previous key is still valid "
        "after it has been rotated.",
    )
    last_login: Optional[datetime] = Field(
        default=None, title="Time when the API key was last used to log in."
    )
    last_rotated: Optional[datetime] = Field(
        default=None, title="Time when the API key was last rotated."
    )

`APIKeyRotateRequest`

Bases: BaseModel

Request model for API key rotation.

Source code in src/zenml/models/v2/core/api_key.py

class APIKeyRotateRequest(BaseModel):
    """Request model for API key rotation."""

    retain_period_minutes: int = Field(
        default=0,
        title="Number of minutes for which the previous key is still valid "
        "after it has been rotated.",
    )

`APIKeyUpdate`

Bases: BaseUpdate

Update model for API keys.

Source code in src/zenml/models/v2/core/api_key.py

class APIKeyUpdate(BaseUpdate):
    """Update model for API keys."""

    name: Optional[str] = Field(
        title="The name of the API Key.",
        max_length=STR_FIELD_MAX_LENGTH,
        default=None,
    )
    description: Optional[str] = Field(
        title="The description of the API Key.",
        max_length=TEXT_FIELD_MAX_LENGTH,
        default=None,
    )
    active: Optional[bool] = Field(
        title="Whether the API key is active.",
        default=None,
    )

`ActionFilter`

Model to enable advanced filtering of all actions.

Source code in src/zenml/models/v2/core/action.py

class ActionFilter(WorkspaceScopedFilter):
    """Model to enable advanced filtering of all actions."""

    name: Optional[str] = Field(
        default=None,
        description="Name of the action.",
    )
    flavor: Optional[str] = Field(
        default=None,
        title="The flavor of the action.",
    )
    plugin_subtype: Optional[str] = Field(
        default=None,
        title="The subtype of the action.",
    )

`ActionFlavorResponse`

Bases: BasePluginFlavorResponse[ActionFlavorResponseBody, ActionFlavorResponseMetadata, ActionFlavorResponseResources]

Response model for Action Flavors.

Source code in src/zenml/models/v2/core/action_flavor.py

class ActionFlavorResponse(
    BasePluginFlavorResponse[
        ActionFlavorResponseBody,
        ActionFlavorResponseMetadata,
        ActionFlavorResponseResources,
    ]
):
    """Response model for Action Flavors."""

    # Body and metadata properties
    @property
    def config_schema(self) -> Dict[str, Any]:
        """The `source_config_schema` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().config_schema

`config_schema` `property`

The source_config_schema property.

Returns:

Type	Description
`Dict[str, Any]`	the value of the property.

`ActionFlavorResponseBody`

Bases: BasePluginResponseBody

Response body for action flavors.

Source code in src/zenml/models/v2/core/action_flavor.py

class ActionFlavorResponseBody(BasePluginResponseBody):
    """Response body for action flavors."""

`ActionFlavorResponseMetadata`

Bases: BasePluginResponseMetadata

Response metadata for action flavors.

Source code in src/zenml/models/v2/core/action_flavor.py

class ActionFlavorResponseMetadata(BasePluginResponseMetadata):
    """Response metadata for action flavors."""

    config_schema: Dict[str, Any]

`ActionFlavorResponseResources`

Bases: BasePluginResponseResources

Response resources for action flavors.

Source code in src/zenml/models/v2/core/action_flavor.py

class ActionFlavorResponseResources(BasePluginResponseResources):
    """Response resources for action flavors."""

`ActionRequest`

Model for creating a new action.

Source code in src/zenml/models/v2/core/action.py

class ActionRequest(WorkspaceScopedRequest):
    """Model for creating a new action."""

    name: str = Field(
        title="The name of the action.", max_length=STR_FIELD_MAX_LENGTH
    )
    description: str = Field(
        default="",
        title="The description of the action",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    flavor: str = Field(
        title="The flavor of the action.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    plugin_subtype: PluginSubType = Field(
        title="The subtype of the action.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    configuration: Dict[str, Any] = Field(
        title="The configuration for the action.",
    )
    service_account_id: UUID = Field(
        title="The service account that is used to execute the action.",
    )
    auth_window: Optional[int] = Field(
        default=None,
        title="The time window in minutes for which the service account is "
        "authorized to execute the action. Set this to 0 to authorize the "
        "service account indefinitely (not recommended). If not set, a "
        "default value defined for each individual action type is used.",
    )

`ActionResponse`

Bases: WorkspaceScopedResponse[ActionResponseBody, ActionResponseMetadata, ActionResponseResources]

Response model for actions.

Source code in src/zenml/models/v2/core/action.py

class ActionResponse(
    WorkspaceScopedResponse[
        ActionResponseBody, ActionResponseMetadata, ActionResponseResources
    ]
):
    """Response model for actions."""

    name: str = Field(
        title="The name of the action.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    def get_hydrated_version(self) -> "ActionResponse":
        """Get the hydrated version of this action.

        Returns:
            An instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_action(self.id)

    # Body and metadata properties
    @property
    def flavor(self) -> str:
        """The `flavor` property.

        Returns:
            the value of the property.
        """
        return self.get_body().flavor

    @property
    def plugin_subtype(self) -> PluginSubType:
        """The `plugin_subtype` property.

        Returns:
            the value of the property.
        """
        return self.get_body().plugin_subtype

    @property
    def description(self) -> str:
        """The `description` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().description

    @property
    def auth_window(self) -> int:
        """The `auth_window` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().auth_window

    @property
    def configuration(self) -> Dict[str, Any]:
        """The `configuration` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().configuration

    def set_configuration(self, configuration: Dict[str, Any]) -> None:
        """Set the `configuration` property.

        Args:
            configuration: The value to set.
        """
        self.get_metadata().configuration = configuration

    # Resource properties
    @property
    def service_account(self) -> "UserResponse":
        """The `service_account` property.

        Returns:
            the value of the property.
        """
        return self.get_resources().service_account

`auth_window` `property`

The auth_window property.

Returns:

Type	Description
`int`	the value of the property.

`configuration` `property`

The configuration property.

Returns:

Type	Description
`Dict[str, Any]`	the value of the property.

`description` `property`

The description property.

Returns:

Type	Description
`str`	the value of the property.

`flavor` `property`

The flavor property.

Returns:

Type	Description
`str`	the value of the property.

`plugin_subtype` `property`

The plugin_subtype property.

Returns:

Type	Description
`PluginSubType`	the value of the property.

`service_account` `property`

The service_account property.

Returns:

Type	Description
`UserResponse`	the value of the property.

`get_hydrated_version()`

Get the hydrated version of this action.

Returns:

Type	Description
`ActionResponse`	An instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/action.py

def get_hydrated_version(self) -> "ActionResponse":
    """Get the hydrated version of this action.

    Returns:
        An instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_action(self.id)

`set_configuration(configuration)`

Set the configuration property.

Parameters:

Name	Type	Description	Default
`configuration`	`Dict[str, Any]`	The value to set.	required

Source code in src/zenml/models/v2/core/action.py

def set_configuration(self, configuration: Dict[str, Any]) -> None:
    """Set the `configuration` property.

    Args:
        configuration: The value to set.
    """
    self.get_metadata().configuration = configuration

`ActionResponseBody`

Response body for actions.

Source code in src/zenml/models/v2/core/action.py

class ActionResponseBody(WorkspaceScopedResponseBody):
    """Response body for actions."""

    flavor: str = Field(
        title="The flavor of the action.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    plugin_subtype: PluginSubType = Field(
        title="The subtype of the action.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

`ActionResponseMetadata`

Response metadata for actions.

Source code in src/zenml/models/v2/core/action.py

class ActionResponseMetadata(WorkspaceScopedResponseMetadata):
    """Response metadata for actions."""

    description: str = Field(
        default="",
        title="The description of the action.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    configuration: Dict[str, Any] = Field(
        title="The configuration for the action.",
    )
    auth_window: int = Field(
        title="The time window in minutes for which the service account is "
        "authorized to execute the action."
    )

`ActionResponseResources`

Class for all resource models associated with the action entity.

Source code in src/zenml/models/v2/core/action.py

class ActionResponseResources(WorkspaceScopedResponseResources):
    """Class for all resource models associated with the action entity."""

    service_account: UserResponse = Field(
        title="The service account that is used to execute the action.",
    )

`ActionUpdate`

Bases: BaseUpdate

Update model for actions.

Source code in src/zenml/models/v2/core/action.py

class ActionUpdate(BaseUpdate):
    """Update model for actions."""

    name: Optional[str] = Field(
        default=None,
        title="The new name for the action.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    description: Optional[str] = Field(
        default=None,
        title="The new description for the action.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    configuration: Optional[Dict[str, Any]] = Field(
        default=None,
        title="The configuration for the action.",
    )
    service_account_id: Optional[UUID] = Field(
        default=None,
        title="The service account that is used to execute the action.",
    )
    auth_window: Optional[int] = Field(
        default=None,
        title="The time window in minutes for which the service account is "
        "authorized to execute the action. Set this to 0 to authorize the "
        "service account indefinitely (not recommended). If not set, a "
        "default value defined for each individual action type is used.",
    )

    @classmethod
    def from_response(cls, response: "ActionResponse") -> "ActionUpdate":
        """Create an update model from a response model.

        Args:
            response: The response model to create the update model from.

        Returns:
            The update model.
        """
        return ActionUpdate(
            configuration=copy.deepcopy(response.configuration),
        )

`from_response(response)` `classmethod`

Create an update model from a response model.

Parameters:

Name	Type	Description	Default
`response`	`ActionResponse`	The response model to create the update model from.	required

Returns:

Type	Description
`ActionUpdate`	The update model.

Source code in src/zenml/models/v2/core/action.py

@classmethod
def from_response(cls, response: "ActionResponse") -> "ActionUpdate":
    """Create an update model from a response model.

    Args:
        response: The response model to create the update model from.

    Returns:
        The update model.
    """
    return ActionUpdate(
        configuration=copy.deepcopy(response.configuration),
    )

`ArtifactFilter`

Bases: TaggableFilter

Model to enable advanced filtering of artifacts.

Source code in src/zenml/models/v2/core/artifact.py

class ArtifactFilter(TaggableFilter):
    """Model to enable advanced filtering of artifacts."""

    name: Optional[str] = None
    has_custom_name: Optional[bool] = None

    CUSTOM_SORTING_OPTIONS: ClassVar[List[str]] = [
        *TaggableFilter.CUSTOM_SORTING_OPTIONS,
        SORT_BY_LATEST_VERSION_KEY,
    ]

    def apply_sorting(
        self,
        query: AnyQuery,
        table: Type["AnySchema"],
    ) -> AnyQuery:
        """Apply sorting to the query for Artifacts.

        Args:
            query: The query to which to apply the sorting.
            table: The query table.

        Returns:
            The query with sorting applied.
        """
        from sqlmodel import asc, case, col, desc, func, select

        from zenml.enums import SorterOps
        from zenml.zen_stores.schemas import (
            ArtifactSchema,
            ArtifactVersionSchema,
        )

        sort_by, operand = self.sorting_params

        if sort_by == SORT_BY_LATEST_VERSION_KEY:
            # Subquery to find the latest version per artifact
            latest_version_subquery = (
                select(
                    ArtifactSchema.id,
                    case(
                        (
                            func.max(ArtifactVersionSchema.created).is_(None),
                            ArtifactSchema.created,
                        ),
                        else_=func.max(ArtifactVersionSchema.created),
                    ).label("latest_version_created"),
                )
                .outerjoin(
                    ArtifactVersionSchema,
                    ArtifactSchema.id == ArtifactVersionSchema.artifact_id,  # type: ignore[arg-type]
                )
                .group_by(col(ArtifactSchema.id))
                .subquery()
            )

            query = query.add_columns(
                latest_version_subquery.c.latest_version_created,
            ).where(ArtifactSchema.id == latest_version_subquery.c.id)

            # Apply sorting based on the operand
            if operand == SorterOps.ASCENDING:
                query = query.order_by(
                    asc(latest_version_subquery.c.latest_version_created),
                    asc(ArtifactSchema.id),
                )
            else:
                query = query.order_by(
                    desc(latest_version_subquery.c.latest_version_created),
                    desc(ArtifactSchema.id),
                )
            return query

        # For other sorting cases, delegate to the parent class
        return super().apply_sorting(query=query, table=table)

`apply_sorting(query, table)`

Apply sorting to the query for Artifacts.

Parameters:

Name	Type	Description	Default
`query`	`AnyQuery`	The query to which to apply the sorting.	required
`table`	`Type[AnySchema]`	The query table.	required

Returns:

Type	Description
`AnyQuery`	The query with sorting applied.

Source code in src/zenml/models/v2/core/artifact.py

def apply_sorting(
    self,
    query: AnyQuery,
    table: Type["AnySchema"],
) -> AnyQuery:
    """Apply sorting to the query for Artifacts.

    Args:
        query: The query to which to apply the sorting.
        table: The query table.

    Returns:
        The query with sorting applied.
    """
    from sqlmodel import asc, case, col, desc, func, select

    from zenml.enums import SorterOps
    from zenml.zen_stores.schemas import (
        ArtifactSchema,
        ArtifactVersionSchema,
    )

    sort_by, operand = self.sorting_params

    if sort_by == SORT_BY_LATEST_VERSION_KEY:
        # Subquery to find the latest version per artifact
        latest_version_subquery = (
            select(
                ArtifactSchema.id,
                case(
                    (
                        func.max(ArtifactVersionSchema.created).is_(None),
                        ArtifactSchema.created,
                    ),
                    else_=func.max(ArtifactVersionSchema.created),
                ).label("latest_version_created"),
            )
            .outerjoin(
                ArtifactVersionSchema,
                ArtifactSchema.id == ArtifactVersionSchema.artifact_id,  # type: ignore[arg-type]
            )
            .group_by(col(ArtifactSchema.id))
            .subquery()
        )

        query = query.add_columns(
            latest_version_subquery.c.latest_version_created,
        ).where(ArtifactSchema.id == latest_version_subquery.c.id)

        # Apply sorting based on the operand
        if operand == SorterOps.ASCENDING:
            query = query.order_by(
                asc(latest_version_subquery.c.latest_version_created),
                asc(ArtifactSchema.id),
            )
        else:
            query = query.order_by(
                desc(latest_version_subquery.c.latest_version_created),
                desc(ArtifactSchema.id),
            )
        return query

    # For other sorting cases, delegate to the parent class
    return super().apply_sorting(query=query, table=table)

`ArtifactRequest`

Bases: BaseRequest

Artifact request model.

Source code in src/zenml/models/v2/core/artifact.py

class ArtifactRequest(BaseRequest):
    """Artifact request model."""

    name: str = Field(
        title="Name of the artifact.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    has_custom_name: bool = Field(
        title="Whether the name is custom (True) or auto-generated (False).",
        default=False,
    )
    tags: Optional[List[str]] = Field(
        title="Artifact tags.",
        description="Should be a list of plain strings, e.g., ['tag1', 'tag2']",
        default=None,
    )

`ArtifactResponse`

Bases: BaseIdentifiedResponse[ArtifactResponseBody, ArtifactResponseMetadata, ArtifactResponseResources]

Artifact response model.

Source code in src/zenml/models/v2/core/artifact.py

class ArtifactResponse(
    BaseIdentifiedResponse[
        ArtifactResponseBody,
        ArtifactResponseMetadata,
        ArtifactResponseResources,
    ]
):
    """Artifact response model."""

    def get_hydrated_version(self) -> "ArtifactResponse":
        """Get the hydrated version of this artifact.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_artifact(self.id)

    name: str = Field(
        title="Name of the output in the parent step.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    # Body and metadata properties
    @property
    def tags(self) -> List[TagResponse]:
        """The `tags` property.

        Returns:
            the value of the property.
        """
        return self.get_body().tags

    @property
    def latest_version_name(self) -> Optional[str]:
        """The `latest_version_name` property.

        Returns:
            the value of the property.
        """
        return self.get_body().latest_version_name

    @property
    def latest_version_id(self) -> Optional[UUID]:
        """The `latest_version_id` property.

        Returns:
            the value of the property.
        """
        return self.get_body().latest_version_id

    @property
    def has_custom_name(self) -> bool:
        """The `has_custom_name` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().has_custom_name

    # Helper methods
    @property
    def versions(self) -> Dict[str, "ArtifactVersionResponse"]:
        """Get a list of all versions of this artifact.

        Returns:
            A list of all versions of this artifact.
        """
        from zenml.client import Client

        responses = Client().list_artifact_versions(name=self.name)
        return {str(response.version): response for response in responses}

`has_custom_name` `property`

The has_custom_name property.

Returns:

Type	Description
`bool`	the value of the property.

`latest_version_id` `property`

The latest_version_id property.

Returns:

Type	Description
`Optional[UUID]`	the value of the property.

`latest_version_name` `property`

The latest_version_name property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`tags` `property`

The tags property.

Returns:

Type	Description
`List[TagResponse]`	the value of the property.

`versions` `property`

Get a list of all versions of this artifact.

Returns:

Type	Description
`Dict[str, ArtifactVersionResponse]`	A list of all versions of this artifact.

`get_hydrated_version()`

Get the hydrated version of this artifact.

Returns:

Type	Description
`ArtifactResponse`	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/artifact.py

def get_hydrated_version(self) -> "ArtifactResponse":
    """Get the hydrated version of this artifact.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_artifact(self.id)

`ArtifactResponseBody`

Response body for artifacts.

Source code in src/zenml/models/v2/core/artifact.py

class ArtifactResponseBody(BaseDatedResponseBody):
    """Response body for artifacts."""

    tags: List[TagResponse] = Field(
        title="Tags associated with the model",
    )
    latest_version_name: Optional[str] = None
    latest_version_id: Optional[UUID] = None

`ArtifactResponseMetadata`

Bases: BaseResponseMetadata

Response metadata for artifacts.

Source code in src/zenml/models/v2/core/artifact.py

class ArtifactResponseMetadata(BaseResponseMetadata):
    """Response metadata for artifacts."""

    has_custom_name: bool = Field(
        title="Whether the name is custom (True) or auto-generated (False).",
        default=False,
    )

`ArtifactUpdate`

Bases: BaseModel

Artifact update model.

Source code in src/zenml/models/v2/core/artifact.py

class ArtifactUpdate(BaseModel):
    """Artifact update model."""

    name: Optional[str] = None
    add_tags: Optional[List[str]] = None
    remove_tags: Optional[List[str]] = None
    has_custom_name: Optional[bool] = None

`ArtifactVersionFilter`

Model to enable advanced filtering of artifact versions.

Source code in src/zenml/models/v2/core/artifact_version.py

class ArtifactVersionFilter(WorkspaceScopedFilter, TaggableFilter):
    """Model to enable advanced filtering of artifact versions."""

    FILTER_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        *WorkspaceScopedFilter.FILTER_EXCLUDE_FIELDS,
        *TaggableFilter.FILTER_EXCLUDE_FIELDS,
        "name",
        "only_unused",
        "has_custom_name",
        "model",
        "pipeline_run",
        "model_version_id",
        "run_metadata",
    ]
    CUSTOM_SORTING_OPTIONS = [
        *WorkspaceScopedFilter.CUSTOM_SORTING_OPTIONS,
        *TaggableFilter.CUSTOM_SORTING_OPTIONS,
    ]
    CLI_EXCLUDE_FIELDS = [
        *WorkspaceScopedFilter.CLI_EXCLUDE_FIELDS,
        *TaggableFilter.CLI_EXCLUDE_FIELDS,
    ]

    artifact_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="ID of the artifact to which this version belongs.",
        union_mode="left_to_right",
    )
    name: Optional[str] = Field(
        default=None,
        description="Name of the artifact to which this version belongs.",
    )
    version: Optional[str] = Field(
        default=None,
        description="Version of the artifact",
    )
    version_number: Optional[Union[int, str]] = Field(
        default=None,
        description="Version of the artifact if it is an integer",
        union_mode="left_to_right",
    )
    uri: Optional[str] = Field(
        default=None,
        description="Uri of the artifact",
    )
    materializer: Optional[str] = Field(
        default=None,
        description="Materializer used to produce the artifact",
    )
    type: Optional[str] = Field(
        default=None,
        description="Type of the artifact",
    )
    data_type: Optional[str] = Field(
        default=None,
        description="Datatype of the artifact",
    )
    artifact_store_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Artifact store for this artifact",
        union_mode="left_to_right",
    )
    model_version_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="ID of the model version that is associated with this "
        "artifact version.",
        union_mode="left_to_right",
    )
    only_unused: Optional[bool] = Field(
        default=False, description="Filter only for unused artifacts"
    )
    has_custom_name: Optional[bool] = Field(
        default=None,
        description="Filter only artifacts with/without custom names.",
    )
    user: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Name/ID of the user that created the artifact version.",
    )
    model: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Name/ID of the model that is associated with this "
        "artifact version.",
    )
    pipeline_run: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Name/ID of a pipeline run that is associated with this "
        "artifact version.",
    )
    run_metadata: Optional[Dict[str, Any]] = Field(
        default=None,
        description="The run_metadata to filter the artifact versions by.",
    )

    model_config = ConfigDict(protected_namespaces=())

    def get_custom_filters(
        self, table: Type["AnySchema"]
    ) -> List[Union["ColumnElement[bool]"]]:
        """Get custom filters.

        Args:
            table: The query table.

        Returns:
            A list of custom filters.
        """
        custom_filters = super().get_custom_filters(table)

        from sqlmodel import and_, or_, select

        from zenml.zen_stores.schemas import (
            ArtifactSchema,
            ArtifactVersionSchema,
            ModelSchema,
            ModelVersionArtifactSchema,
            ModelVersionSchema,
            PipelineRunSchema,
            RunMetadataResourceSchema,
            RunMetadataSchema,
            StepRunInputArtifactSchema,
            StepRunOutputArtifactSchema,
            StepRunSchema,
        )

        if self.name:
            value, filter_operator = self._resolve_operator(self.name)
            filter_ = StrFilter(
                operation=GenericFilterOps(filter_operator),
                column="name",
                value=value,
            )
            artifact_name_filter = and_(
                ArtifactVersionSchema.artifact_id == ArtifactSchema.id,
                filter_.generate_query_conditions(ArtifactSchema),
            )
            custom_filters.append(artifact_name_filter)

        if self.only_unused:
            unused_filter = and_(
                ArtifactVersionSchema.id.notin_(  # type: ignore[attr-defined]
                    select(StepRunOutputArtifactSchema.artifact_id)
                ),
                ArtifactVersionSchema.id.notin_(  # type: ignore[attr-defined]
                    select(StepRunInputArtifactSchema.artifact_id)
                ),
            )
            custom_filters.append(unused_filter)

        if self.model_version_id:
            value, operator = self._resolve_operator(self.model_version_id)

            model_version_filter = and_(
                ArtifactVersionSchema.id
                == ModelVersionArtifactSchema.artifact_version_id,
                ModelVersionArtifactSchema.model_version_id
                == ModelVersionSchema.id,
                FilterGenerator(ModelVersionSchema)
                .define_filter(column="id", value=value, operator=operator)
                .generate_query_conditions(ModelVersionSchema),
            )
            custom_filters.append(model_version_filter)

        if self.has_custom_name is not None:
            custom_name_filter = and_(
                ArtifactVersionSchema.artifact_id == ArtifactSchema.id,
                ArtifactSchema.has_custom_name == self.has_custom_name,
            )
            custom_filters.append(custom_name_filter)

        if self.model:
            model_filter = and_(
                ArtifactVersionSchema.id
                == ModelVersionArtifactSchema.artifact_version_id,
                ModelVersionArtifactSchema.model_version_id
                == ModelVersionSchema.id,
                ModelVersionSchema.model_id == ModelSchema.id,
                self.generate_name_or_id_query_conditions(
                    value=self.model, table=ModelSchema
                ),
            )
            custom_filters.append(model_filter)

        if self.pipeline_run:
            pipeline_run_filter = and_(
                or_(
                    and_(
                        ArtifactVersionSchema.id
                        == StepRunOutputArtifactSchema.artifact_id,
                        StepRunOutputArtifactSchema.step_id
                        == StepRunSchema.id,
                    ),
                    and_(
                        ArtifactVersionSchema.id
                        == StepRunInputArtifactSchema.artifact_id,
                        StepRunInputArtifactSchema.step_id == StepRunSchema.id,
                    ),
                ),
                StepRunSchema.pipeline_run_id == PipelineRunSchema.id,
                self.generate_name_or_id_query_conditions(
                    value=self.pipeline_run, table=PipelineRunSchema
                ),
            )
            custom_filters.append(pipeline_run_filter)

        if self.run_metadata is not None:
            from zenml.enums import MetadataResourceTypes

            for key, value in self.run_metadata.items():
                additional_filter = and_(
                    RunMetadataResourceSchema.resource_id
                    == ArtifactVersionSchema.id,
                    RunMetadataResourceSchema.resource_type
                    == MetadataResourceTypes.ARTIFACT_VERSION.value,
                    RunMetadataResourceSchema.run_metadata_id
                    == RunMetadataSchema.id,
                    self.generate_custom_query_conditions_for_column(
                        value=key,
                        table=RunMetadataSchema,
                        column="key",
                    ),
                    self.generate_custom_query_conditions_for_column(
                        value=value,
                        table=RunMetadataSchema,
                        column="value",
                        json_encode_value=True,
                    ),
                )
                custom_filters.append(additional_filter)

        return custom_filters

`get_custom_filters(table)`

Get custom filters.

Parameters:

Name	Type	Description	Default
`table`	`Type[AnySchema]`	The query table.	required

Returns:

Type	Description
`List[Union[ColumnElement[bool]]]`	A list of custom filters.

Source code in src/zenml/models/v2/core/artifact_version.py

def get_custom_filters(
    self, table: Type["AnySchema"]
) -> List[Union["ColumnElement[bool]"]]:
    """Get custom filters.

    Args:
        table: The query table.

    Returns:
        A list of custom filters.
    """
    custom_filters = super().get_custom_filters(table)

    from sqlmodel import and_, or_, select

    from zenml.zen_stores.schemas import (
        ArtifactSchema,
        ArtifactVersionSchema,
        ModelSchema,
        ModelVersionArtifactSchema,
        ModelVersionSchema,
        PipelineRunSchema,
        RunMetadataResourceSchema,
        RunMetadataSchema,
        StepRunInputArtifactSchema,
        StepRunOutputArtifactSchema,
        StepRunSchema,
    )

    if self.name:
        value, filter_operator = self._resolve_operator(self.name)
        filter_ = StrFilter(
            operation=GenericFilterOps(filter_operator),
            column="name",
            value=value,
        )
        artifact_name_filter = and_(
            ArtifactVersionSchema.artifact_id == ArtifactSchema.id,
            filter_.generate_query_conditions(ArtifactSchema),
        )
        custom_filters.append(artifact_name_filter)

    if self.only_unused:
        unused_filter = and_(
            ArtifactVersionSchema.id.notin_(  # type: ignore[attr-defined]
                select(StepRunOutputArtifactSchema.artifact_id)
            ),
            ArtifactVersionSchema.id.notin_(  # type: ignore[attr-defined]
                select(StepRunInputArtifactSchema.artifact_id)
            ),
        )
        custom_filters.append(unused_filter)

    if self.model_version_id:
        value, operator = self._resolve_operator(self.model_version_id)

        model_version_filter = and_(
            ArtifactVersionSchema.id
            == ModelVersionArtifactSchema.artifact_version_id,
            ModelVersionArtifactSchema.model_version_id
            == ModelVersionSchema.id,
            FilterGenerator(ModelVersionSchema)
            .define_filter(column="id", value=value, operator=operator)
            .generate_query_conditions(ModelVersionSchema),
        )
        custom_filters.append(model_version_filter)

    if self.has_custom_name is not None:
        custom_name_filter = and_(
            ArtifactVersionSchema.artifact_id == ArtifactSchema.id,
            ArtifactSchema.has_custom_name == self.has_custom_name,
        )
        custom_filters.append(custom_name_filter)

    if self.model:
        model_filter = and_(
            ArtifactVersionSchema.id
            == ModelVersionArtifactSchema.artifact_version_id,
            ModelVersionArtifactSchema.model_version_id
            == ModelVersionSchema.id,
            ModelVersionSchema.model_id == ModelSchema.id,
            self.generate_name_or_id_query_conditions(
                value=self.model, table=ModelSchema
            ),
        )
        custom_filters.append(model_filter)

    if self.pipeline_run:
        pipeline_run_filter = and_(
            or_(
                and_(
                    ArtifactVersionSchema.id
                    == StepRunOutputArtifactSchema.artifact_id,
                    StepRunOutputArtifactSchema.step_id
                    == StepRunSchema.id,
                ),
                and_(
                    ArtifactVersionSchema.id
                    == StepRunInputArtifactSchema.artifact_id,
                    StepRunInputArtifactSchema.step_id == StepRunSchema.id,
                ),
            ),
            StepRunSchema.pipeline_run_id == PipelineRunSchema.id,
            self.generate_name_or_id_query_conditions(
                value=self.pipeline_run, table=PipelineRunSchema
            ),
        )
        custom_filters.append(pipeline_run_filter)

    if self.run_metadata is not None:
        from zenml.enums import MetadataResourceTypes

        for key, value in self.run_metadata.items():
            additional_filter = and_(
                RunMetadataResourceSchema.resource_id
                == ArtifactVersionSchema.id,
                RunMetadataResourceSchema.resource_type
                == MetadataResourceTypes.ARTIFACT_VERSION.value,
                RunMetadataResourceSchema.run_metadata_id
                == RunMetadataSchema.id,
                self.generate_custom_query_conditions_for_column(
                    value=key,
                    table=RunMetadataSchema,
                    column="key",
                ),
                self.generate_custom_query_conditions_for_column(
                    value=value,
                    table=RunMetadataSchema,
                    column="value",
                    json_encode_value=True,
                ),
            )
            custom_filters.append(additional_filter)

    return custom_filters

`ArtifactVersionRequest`

Request model for artifact versions.

Source code in src/zenml/models/v2/core/artifact_version.py

class ArtifactVersionRequest(WorkspaceScopedRequest):
    """Request model for artifact versions."""

    artifact_id: Optional[UUID] = Field(
        default=None,
        title="ID of the artifact to which this version belongs.",
    )
    artifact_name: Optional[str] = Field(
        default=None,
        title="Name of the artifact to which this version belongs.",
    )
    version: Optional[Union[int, str]] = Field(
        default=None, title="Version of the artifact."
    )
    has_custom_name: bool = Field(
        title="Whether the name is custom (True) or auto-generated (False).",
        default=False,
    )
    type: ArtifactType = Field(title="Type of the artifact.")
    artifact_store_id: Optional[UUID] = Field(
        title="ID of the artifact store in which this artifact is stored.",
        default=None,
    )
    uri: str = Field(
        title="URI of the artifact.", max_length=TEXT_FIELD_MAX_LENGTH
    )
    materializer: SourceWithValidator = Field(
        title="Materializer class to use for this artifact.",
    )
    data_type: SourceWithValidator = Field(
        title="Data type of the artifact.",
    )
    tags: Optional[List[str]] = Field(
        title="Tags of the artifact.",
        description="Should be a list of plain strings, e.g., ['tag1', 'tag2']",
        default=None,
    )
    visualizations: Optional[List["ArtifactVisualizationRequest"]] = Field(
        default=None, title="Visualizations of the artifact."
    )
    save_type: ArtifactSaveType = Field(
        title="The save type of the artifact version.",
    )
    metadata: Optional[Dict[str, MetadataType]] = Field(
        default=None, title="Metadata of the artifact version."
    )

    @field_validator("version")
    @classmethod
    def str_field_max_length_check(cls, value: Any) -> Any:
        """Checks if the length of the value exceeds the maximum str length.

        Args:
            value: the value set in the field

        Returns:
            the value itself.

        Raises:
            AssertionError: if the length of the field is longer than the
                maximum threshold.
        """
        assert len(str(value)) < STR_FIELD_MAX_LENGTH, (
            "The length of the value for this field can not "
            f"exceed {STR_FIELD_MAX_LENGTH}"
        )
        return value

    @model_validator(mode="after")
    def _validate_request(self) -> "ArtifactVersionRequest":
        """Validate the request values.

        Raises:
            ValueError: If the request is invalid.

        Returns:
            The validated request.
        """
        if self.artifact_id and self.artifact_name:
            raise ValueError(
                "Only one of artifact_name and artifact_id can be set."
            )

        if not (self.artifact_id or self.artifact_name):
            raise ValueError(
                "Either artifact_name or artifact_id must be set."
            )

        return self

`str_field_max_length_check(value)` `classmethod`

Checks if the length of the value exceeds the maximum str length.

Parameters:

Name	Type	Description	Default
`value`	`Any`	the value set in the field	required

Returns:

Type	Description
`Any`	the value itself.

Raises:

Type	Description
`AssertionError`	if the length of the field is longer than the maximum threshold.

Source code in src/zenml/models/v2/core/artifact_version.py

@field_validator("version")
@classmethod
def str_field_max_length_check(cls, value: Any) -> Any:
    """Checks if the length of the value exceeds the maximum str length.

    Args:
        value: the value set in the field

    Returns:
        the value itself.

    Raises:
        AssertionError: if the length of the field is longer than the
            maximum threshold.
    """
    assert len(str(value)) < STR_FIELD_MAX_LENGTH, (
        "The length of the value for this field can not "
        f"exceed {STR_FIELD_MAX_LENGTH}"
    )
    return value

`ArtifactVersionResponse`

Bases: WorkspaceScopedResponse[ArtifactVersionResponseBody, ArtifactVersionResponseMetadata, ArtifactVersionResponseResources]

Response model for artifact versions.

Source code in src/zenml/models/v2/core/artifact_version.py

class ArtifactVersionResponse(
    WorkspaceScopedResponse[
        ArtifactVersionResponseBody,
        ArtifactVersionResponseMetadata,
        ArtifactVersionResponseResources,
    ]
):
    """Response model for artifact versions."""

    def get_hydrated_version(self) -> "ArtifactVersionResponse":
        """Get the hydrated version of this artifact version.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_artifact_version(self.id)

    # Body and metadata properties
    @property
    def artifact(self) -> "ArtifactResponse":
        """The `artifact` property.

        Returns:
            the value of the property.
        """
        return self.get_body().artifact

    @property
    def version(self) -> Union[str, int]:
        """The `version` property.

        Returns:
            the value of the property.
        """
        return self.get_body().version

    @property
    def uri(self) -> str:
        """The `uri` property.

        Returns:
            the value of the property.
        """
        return self.get_body().uri

    @property
    def type(self) -> ArtifactType:
        """The `type` property.

        Returns:
            the value of the property.
        """
        return self.get_body().type

    @property
    def tags(self) -> List[TagResponse]:
        """The `tags` property.

        Returns:
            the value of the property.
        """
        return self.get_body().tags

    @property
    def producer_pipeline_run_id(self) -> Optional[UUID]:
        """The `producer_pipeline_run_id` property.

        Returns:
            the value of the property.
        """
        return self.get_body().producer_pipeline_run_id

    @property
    def save_type(self) -> ArtifactSaveType:
        """The `save_type` property.

        Returns:
            the value of the property.
        """
        return self.get_body().save_type

    @property
    def artifact_store_id(self) -> Optional[UUID]:
        """The `artifact_store_id` property.

        Returns:
            the value of the property.
        """
        return self.get_body().artifact_store_id

    @property
    def producer_step_run_id(self) -> Optional[UUID]:
        """The `producer_step_run_id` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().producer_step_run_id

    @property
    def visualizations(
        self,
    ) -> Optional[List["ArtifactVisualizationResponse"]]:
        """The `visualizations` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().visualizations

    @property
    def run_metadata(self) -> Dict[str, MetadataType]:
        """The `metadata` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().run_metadata

    @property
    def materializer(self) -> Source:
        """The `materializer` property.

        Returns:
            the value of the property.
        """
        return self.get_body().materializer

    @property
    def data_type(self) -> Source:
        """The `data_type` property.

        Returns:
            the value of the property.
        """
        return self.get_body().data_type

    # Helper methods
    @property
    def name(self) -> str:
        """The `name` property.

        Returns:
            the value of the property.
        """
        return self.artifact.name

    @property
    def step(self) -> "StepRunResponse":
        """Get the step that produced this artifact.

        Returns:
            The step that produced this artifact.
        """
        from zenml.artifacts.utils import get_producer_step_of_artifact

        return get_producer_step_of_artifact(self)

    @property
    def run(self) -> "PipelineRunResponse":
        """Get the pipeline run that produced this artifact.

        Returns:
            The pipeline run that produced this artifact.
        """
        from zenml.client import Client

        return Client().get_pipeline_run(self.step.pipeline_run_id)

    def load(self) -> Any:
        """Materializes (loads) the data stored in this artifact.

        Returns:
            The materialized data.
        """
        from zenml.artifacts.utils import load_artifact_from_response

        return load_artifact_from_response(self)

    def download_files(self, path: str, overwrite: bool = False) -> None:
        """Downloads data for an artifact with no materializing.

        Any artifacts will be saved as a zip file to the given path.

        Args:
            path: The path to save the binary data to.
            overwrite: Whether to overwrite the file if it already exists.

        Raises:
            ValueError: If the path does not end with '.zip'.
        """
        if not path.endswith(".zip"):
            raise ValueError(
                "The path should end with '.zip' to save the binary data."
            )
        from zenml.artifacts.utils import (
            download_artifact_files_from_response,
        )

        download_artifact_files_from_response(
            self,
            path=path,
            overwrite=overwrite,
        )

    def visualize(self, title: Optional[str] = None) -> None:
        """Visualize the artifact in notebook environments.

        Args:
            title: Optional title to show before the visualizations.
        """
        from zenml.utils.visualization_utils import visualize_artifact

        visualize_artifact(self, title=title)

`artifact` `property`

The artifact property.

Returns:

Type	Description
`ArtifactResponse`	the value of the property.

`artifact_store_id` `property`

The artifact_store_id property.

Returns:

Type	Description
`Optional[UUID]`	the value of the property.

`data_type` `property`

The data_type property.

Returns:

Type	Description
`Source`	the value of the property.

`materializer` `property`

The materializer property.

Returns:

Type	Description
`Source`	the value of the property.

`name` `property`

The name property.

Returns:

Type	Description
`str`	the value of the property.

`producer_pipeline_run_id` `property`

The producer_pipeline_run_id property.

Returns:

Type	Description
`Optional[UUID]`	the value of the property.

`producer_step_run_id` `property`

The producer_step_run_id property.

Returns:

Type	Description
`Optional[UUID]`	the value of the property.

`run` `property`

Get the pipeline run that produced this artifact.

Returns:

Type	Description
`PipelineRunResponse`	The pipeline run that produced this artifact.

`run_metadata` `property`

The metadata property.

Returns:

Type	Description
`Dict[str, MetadataType]`	the value of the property.

`save_type` `property`

The save_type property.

Returns:

Type	Description
`ArtifactSaveType`	the value of the property.

`step` `property`

Get the step that produced this artifact.

Returns:

Type	Description
`StepRunResponse`	The step that produced this artifact.

`tags` `property`

The tags property.

Returns:

Type	Description
`List[TagResponse]`	the value of the property.

`type` `property`

The type property.

Returns:

Type	Description
`ArtifactType`	the value of the property.

`uri` `property`

The uri property.

Returns:

Type	Description
`str`	the value of the property.

`version` `property`

The version property.

Returns:

Type	Description
`Union[str, int]`	the value of the property.

`visualizations` `property`

The visualizations property.

Returns:

Type	Description
`Optional[List[ArtifactVisualizationResponse]]`	the value of the property.

`download_files(path, overwrite=False)`

Downloads data for an artifact with no materializing.

Any artifacts will be saved as a zip file to the given path.

Parameters:

Name	Type	Description	Default
`path`	`str`	The path to save the binary data to.	required
`overwrite`	`bool`	Whether to overwrite the file if it already exists.	`False`

Raises:

Type	Description
`ValueError`	If the path does not end with '.zip'.

Source code in src/zenml/models/v2/core/artifact_version.py

def download_files(self, path: str, overwrite: bool = False) -> None:
    """Downloads data for an artifact with no materializing.

    Any artifacts will be saved as a zip file to the given path.

    Args:
        path: The path to save the binary data to.
        overwrite: Whether to overwrite the file if it already exists.

    Raises:
        ValueError: If the path does not end with '.zip'.
    """
    if not path.endswith(".zip"):
        raise ValueError(
            "The path should end with '.zip' to save the binary data."
        )
    from zenml.artifacts.utils import (
        download_artifact_files_from_response,
    )

    download_artifact_files_from_response(
        self,
        path=path,
        overwrite=overwrite,
    )

`get_hydrated_version()`

Get the hydrated version of this artifact version.

Returns:

Type	Description
`ArtifactVersionResponse`	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/artifact_version.py

def get_hydrated_version(self) -> "ArtifactVersionResponse":
    """Get the hydrated version of this artifact version.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_artifact_version(self.id)

`load()`

Materializes (loads) the data stored in this artifact.

Returns:

Type	Description
`Any`	The materialized data.

Source code in src/zenml/models/v2/core/artifact_version.py

def load(self) -> Any:
    """Materializes (loads) the data stored in this artifact.

    Returns:
        The materialized data.
    """
    from zenml.artifacts.utils import load_artifact_from_response

    return load_artifact_from_response(self)

`visualize(title=None)`

Visualize the artifact in notebook environments.

Parameters:

Name	Type	Description	Default
`title`	`Optional[str]`	Optional title to show before the visualizations.	`None`

Source code in src/zenml/models/v2/core/artifact_version.py

def visualize(self, title: Optional[str] = None) -> None:
    """Visualize the artifact in notebook environments.

    Args:
        title: Optional title to show before the visualizations.
    """
    from zenml.utils.visualization_utils import visualize_artifact

    visualize_artifact(self, title=title)

`ArtifactVersionResponseBody`

Response body for artifact versions.

Source code in src/zenml/models/v2/core/artifact_version.py

class ArtifactVersionResponseBody(WorkspaceScopedResponseBody):
    """Response body for artifact versions."""

    artifact: ArtifactResponse = Field(
        title="Artifact to which this version belongs."
    )
    version: str = Field(title="Version of the artifact.")
    uri: str = Field(
        title="URI of the artifact.", max_length=TEXT_FIELD_MAX_LENGTH
    )
    type: ArtifactType = Field(title="Type of the artifact.")
    materializer: SourceWithValidator = Field(
        title="Materializer class to use for this artifact.",
    )
    data_type: SourceWithValidator = Field(
        title="Data type of the artifact.",
    )
    tags: List[TagResponse] = Field(
        title="Tags associated with the model",
    )
    producer_pipeline_run_id: Optional[UUID] = Field(
        title="The ID of the pipeline run that generated this artifact version.",
        default=None,
    )
    save_type: ArtifactSaveType = Field(
        title="The save type of the artifact version.",
    )
    artifact_store_id: Optional[UUID] = Field(
        title="ID of the artifact store in which this artifact is stored.",
        default=None,
    )

    @field_validator("version")
    @classmethod
    def str_field_max_length_check(cls, value: Any) -> Any:
        """Checks if the length of the value exceeds the maximum str length.

        Args:
            value: the value set in the field

        Returns:
            the value itself.

        Raises:
            AssertionError: if the length of the field is longer than the
                maximum threshold.
        """
        assert len(str(value)) < STR_FIELD_MAX_LENGTH, (
            "The length of the value for this field can not "
            f"exceed {STR_FIELD_MAX_LENGTH}"
        )
        return value

`str_field_max_length_check(value)` `classmethod`

Checks if the length of the value exceeds the maximum str length.

Parameters:

Name	Type	Description	Default
`value`	`Any`	the value set in the field	required

Returns:

Type	Description
`Any`	the value itself.

Raises:

Type	Description
`AssertionError`	if the length of the field is longer than the maximum threshold.

Source code in src/zenml/models/v2/core/artifact_version.py

@field_validator("version")
@classmethod
def str_field_max_length_check(cls, value: Any) -> Any:
    """Checks if the length of the value exceeds the maximum str length.

    Args:
        value: the value set in the field

    Returns:
        the value itself.

    Raises:
        AssertionError: if the length of the field is longer than the
            maximum threshold.
    """
    assert len(str(value)) < STR_FIELD_MAX_LENGTH, (
        "The length of the value for this field can not "
        f"exceed {STR_FIELD_MAX_LENGTH}"
    )
    return value

`ArtifactVersionResponseMetadata`

Response metadata for artifact versions.

Source code in src/zenml/models/v2/core/artifact_version.py

class ArtifactVersionResponseMetadata(WorkspaceScopedResponseMetadata):
    """Response metadata for artifact versions."""

    producer_step_run_id: Optional[UUID] = Field(
        title="ID of the step run that produced this artifact.",
        default=None,
    )
    visualizations: Optional[List["ArtifactVisualizationResponse"]] = Field(
        default=None, title="Visualizations of the artifact."
    )
    run_metadata: Dict[str, MetadataType] = Field(
        default={}, title="Metadata of the artifact."
    )

`ArtifactVersionUpdate`

Bases: BaseModel

Artifact version update model.

Source code in src/zenml/models/v2/core/artifact_version.py

class ArtifactVersionUpdate(BaseModel):
    """Artifact version update model."""

    name: Optional[str] = None
    add_tags: Optional[List[str]] = None
    remove_tags: Optional[List[str]] = None

`ArtifactVisualizationRequest`

Bases: BaseRequest

Request model for artifact visualization.

Source code in src/zenml/models/v2/core/artifact_visualization.py

class ArtifactVisualizationRequest(BaseRequest):
    """Request model for artifact visualization."""

    type: VisualizationType
    uri: str

`ArtifactVisualizationResponse`

Bases: BaseIdentifiedResponse[ArtifactVisualizationResponseBody, ArtifactVisualizationResponseMetadata, ArtifactVisualizationResponseResources]

Response model for artifact visualizations.

Source code in src/zenml/models/v2/core/artifact_visualization.py

class ArtifactVisualizationResponse(
    BaseIdentifiedResponse[
        ArtifactVisualizationResponseBody,
        ArtifactVisualizationResponseMetadata,
        ArtifactVisualizationResponseResources,
    ]
):
    """Response model for artifact visualizations."""

    def get_hydrated_version(self) -> "ArtifactVisualizationResponse":
        """Get the hydrated version of this artifact visualization.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_artifact_visualization(self.id)

    # Body and metadata properties
    @property
    def type(self) -> VisualizationType:
        """The `type` property.

        Returns:
            the value of the property.
        """
        return self.get_body().type

    @property
    def uri(self) -> str:
        """The `uri` property.

        Returns:
            the value of the property.
        """
        return self.get_body().uri

    @property
    def artifact_version_id(self) -> UUID:
        """The `artifact_version_id` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().artifact_version_id

`artifact_version_id` `property`

The artifact_version_id property.

Returns:

Type	Description
`UUID`	the value of the property.

`type` `property`

The type property.

Returns:

Type	Description
`VisualizationType`	the value of the property.

`uri` `property`

The uri property.

Returns:

Type	Description
`str`	the value of the property.

`get_hydrated_version()`

Get the hydrated version of this artifact visualization.

Returns:

Type	Description
`ArtifactVisualizationResponse`	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/artifact_visualization.py

def get_hydrated_version(self) -> "ArtifactVisualizationResponse":
    """Get the hydrated version of this artifact visualization.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_artifact_visualization(self.id)

`ArtifactVisualizationResponseBody`

Response body for artifact visualizations.

Source code in src/zenml/models/v2/core/artifact_visualization.py

class ArtifactVisualizationResponseBody(BaseDatedResponseBody):
    """Response body for artifact visualizations."""

    type: VisualizationType
    uri: str

`ArtifactVisualizationResponseMetadata`

Bases: BaseResponseMetadata

Response metadata model for artifact visualizations.

Source code in src/zenml/models/v2/core/artifact_visualization.py

class ArtifactVisualizationResponseMetadata(BaseResponseMetadata):
    """Response metadata model for artifact visualizations."""

    artifact_version_id: UUID

`AuthenticationMethodModel`

Bases: BaseModel

Authentication method specification.

Describes the schema for the configuration and secrets that need to be provided to configure an authentication method.

Source code in src/zenml/models/v2/misc/service_connector_type.py

class AuthenticationMethodModel(BaseModel):
    """Authentication method specification.

    Describes the schema for the configuration and secrets that need to be
    provided to configure an authentication method.
    """

    name: str = Field(
        title="User readable name for the authentication method.",
    )
    auth_method: str = Field(
        title="The name of the authentication method.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    description: str = Field(
        default="",
        title="A description of the authentication method.",
    )
    config_schema: Dict[str, Any] = Field(
        default_factory=dict,
        title="The JSON schema of the configuration for this authentication "
        "method.",
    )
    min_expiration_seconds: Optional[int] = Field(
        default=None,
        title="The minimum number of seconds that the authentication "
        "session can be configured to be valid for. Set to None for "
        "authentication sessions and long-lived credentials that don't expire.",
    )
    max_expiration_seconds: Optional[int] = Field(
        default=None,
        title="The maximum number of seconds that the authentication "
        "session can be configured to be valid for. Set to None for "
        "authentication sessions and long-lived credentials that don't expire.",
    )
    default_expiration_seconds: Optional[int] = Field(
        default=None,
        title="The default number of seconds that the authentication "
        "session is valid for. Set to None for authentication sessions and "
        "long-lived credentials that don't expire.",
    )
    _config_class: Optional[Type[BaseModel]] = None

    def __init__(
        self, config_class: Optional[Type[BaseModel]] = None, **values: Any
    ):
        """Initialize the authentication method.

        Args:
            config_class: The configuration class for the authentication
                method.
            **values: The data to initialize the authentication method with.
        """
        if config_class:
            values["config_schema"] = config_class.model_json_schema()

        super().__init__(**values)
        self._config_class = config_class

    @property
    def config_class(self) -> Optional[Type[BaseModel]]:
        """Get the configuration class for the authentication method.

        Returns:
            The configuration class for the authentication method.
        """
        return self._config_class

    def supports_temporary_credentials(self) -> bool:
        """Check if the authentication method supports temporary credentials.

        Returns:
            True if the authentication method supports temporary credentials,
            False otherwise.
        """
        return (
            self.min_expiration_seconds is not None
            or self.max_expiration_seconds is not None
            or self.default_expiration_seconds is not None
        )

    def validate_expiration(
        self, expiration_seconds: Optional[int]
    ) -> Optional[int]:
        """Validate the expiration time.

        Args:
            expiration_seconds: The expiration time in seconds. If None, the
                default expiration time is used, if applicable.

        Returns:
            The expiration time in seconds or None if not applicable.

        Raises:
            ValueError: If the expiration time is not valid.
        """
        if not self.supports_temporary_credentials():
            if expiration_seconds is not None:
                # Expiration is not supported
                raise ValueError(
                    "Expiration time is not supported for this authentication "
                    f"method but a value was provided: {expiration_seconds}"
                )

            return None

        expiration_seconds = (
            expiration_seconds or self.default_expiration_seconds
        )

        if expiration_seconds is None:
            return None

        if self.min_expiration_seconds is not None:
            if expiration_seconds < self.min_expiration_seconds:
                raise ValueError(
                    f"Expiration time must be at least "
                    f"{self.min_expiration_seconds} seconds."
                )

        if self.max_expiration_seconds is not None:
            if expiration_seconds > self.max_expiration_seconds:
                raise ValueError(
                    f"Expiration time must be at most "
                    f"{self.max_expiration_seconds} seconds."
                )

        return expiration_seconds

`config_class` `property`

Get the configuration class for the authentication method.

Returns:

Type	Description
`Optional[Type[BaseModel]]`	The configuration class for the authentication method.

`init(config_class=None, **values)`

Initialize the authentication method.

Parameters:

Name	Type	Description	Default
`config_class`	`Optional[Type[BaseModel]]`	The configuration class for the authentication method.	`None`
`**values`	`Any`	The data to initialize the authentication method with.	`{}`

Source code in src/zenml/models/v2/misc/service_connector_type.py

def __init__(
    self, config_class: Optional[Type[BaseModel]] = None, **values: Any
):
    """Initialize the authentication method.

    Args:
        config_class: The configuration class for the authentication
            method.
        **values: The data to initialize the authentication method with.
    """
    if config_class:
        values["config_schema"] = config_class.model_json_schema()

    super().__init__(**values)
    self._config_class = config_class

`supports_temporary_credentials()`

Check if the authentication method supports temporary credentials.

Returns:

Type	Description
`bool`	True if the authentication method supports temporary credentials,
`bool`	False otherwise.

Source code in src/zenml/models/v2/misc/service_connector_type.py

def supports_temporary_credentials(self) -> bool:
    """Check if the authentication method supports temporary credentials.

    Returns:
        True if the authentication method supports temporary credentials,
        False otherwise.
    """
    return (
        self.min_expiration_seconds is not None
        or self.max_expiration_seconds is not None
        or self.default_expiration_seconds is not None
    )

`validate_expiration(expiration_seconds)`

Validate the expiration time.

Parameters:

Name	Type	Description	Default
`expiration_seconds`	`Optional[int]`	The expiration time in seconds. If None, the default expiration time is used, if applicable.	required

Returns:

Type	Description
`Optional[int]`	The expiration time in seconds or None if not applicable.

Raises:

Type	Description
`ValueError`	If the expiration time is not valid.

Source code in src/zenml/models/v2/misc/service_connector_type.py

def validate_expiration(
    self, expiration_seconds: Optional[int]
) -> Optional[int]:
    """Validate the expiration time.

    Args:
        expiration_seconds: The expiration time in seconds. If None, the
            default expiration time is used, if applicable.

    Returns:
        The expiration time in seconds or None if not applicable.

    Raises:
        ValueError: If the expiration time is not valid.
    """
    if not self.supports_temporary_credentials():
        if expiration_seconds is not None:
            # Expiration is not supported
            raise ValueError(
                "Expiration time is not supported for this authentication "
                f"method but a value was provided: {expiration_seconds}"
            )

        return None

    expiration_seconds = (
        expiration_seconds or self.default_expiration_seconds
    )

    if expiration_seconds is None:
        return None

    if self.min_expiration_seconds is not None:
        if expiration_seconds < self.min_expiration_seconds:
            raise ValueError(
                f"Expiration time must be at least "
                f"{self.min_expiration_seconds} seconds."
            )

    if self.max_expiration_seconds is not None:
        if expiration_seconds > self.max_expiration_seconds:
            raise ValueError(
                f"Expiration time must be at most "
                f"{self.max_expiration_seconds} seconds."
            )

    return expiration_seconds

`BaseDatedResponseBody`

Bases: BaseResponseBody

Base body model for entities that track a creation and update timestamp.

Used as a base class for all body models associated with responses. Features a creation and update timestamp.

Source code in src/zenml/models/v2/base/base.py

class BaseDatedResponseBody(BaseResponseBody):
    """Base body model for entities that track a creation and update timestamp.

    Used as a base class for all body models associated with responses.
    Features a creation and update timestamp.
    """

    created: datetime = Field(
        title="The timestamp when this resource was created."
    )
    updated: datetime = Field(
        title="The timestamp when this resource was last updated."
    )

`BaseFilter`

Bases: BaseModel

Class to unify all filter, paginate and sort request parameters.

This Model allows fine-grained filtering, sorting and pagination of resources.

Usage example for subclasses of this class:

ResourceListModel(
    name="contains:default",
    workspace="default"
    count_steps="gte:5"
    sort_by="created",
    page=2,
    size=20
)

Source code in src/zenml/models/v2/base/filter.py

class BaseFilter(BaseModel):
    """Class to unify all filter, paginate and sort request parameters.

    This Model allows fine-grained filtering, sorting and pagination of
    resources.

    Usage example for subclasses of this class:
    ```
    ResourceListModel(
        name="contains:default",
        workspace="default"
        count_steps="gte:5"
        sort_by="created",
        page=2,
        size=20
    )
    ```
    """

    # List of fields that cannot be used as filters.
    FILTER_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        "sort_by",
        "page",
        "size",
        "logical_operator",
    ]
    CUSTOM_SORTING_OPTIONS: ClassVar[List[str]] = []

    # List of fields that are not even mentioned as options in the CLI.
    CLI_EXCLUDE_FIELDS: ClassVar[List[str]] = []

    # List of fields that are wrapped with `fastapi.Query(default)` in API.
    API_MULTI_INPUT_PARAMS: ClassVar[List[str]] = []

    sort_by: str = Field(
        default="created", description="Which column to sort by."
    )
    logical_operator: LogicalOperators = Field(
        default=LogicalOperators.AND,
        description="Which logical operator to use between all filters "
        "['and', 'or']",
    )
    page: int = Field(
        default=PAGINATION_STARTING_PAGE, ge=1, description="Page number"
    )
    size: int = Field(
        default=PAGE_SIZE_DEFAULT,
        ge=1,
        le=PAGE_SIZE_MAXIMUM,
        description="Page size",
    )
    id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Id for this resource",
        union_mode="left_to_right",
    )
    created: Optional[Union[datetime, str]] = Field(
        default=None, description="Created", union_mode="left_to_right"
    )
    updated: Optional[Union[datetime, str]] = Field(
        default=None, description="Updated", union_mode="left_to_right"
    )

    _rbac_configuration: Optional[
        Tuple[UUID, Dict[str, Optional[Set[UUID]]]]
    ] = None

    @field_validator("sort_by", mode="before")
    @classmethod
    def validate_sort_by(cls, value: Any) -> Any:
        """Validate that the sort_column is a valid column with a valid operand.

        Args:
            value: The sort_by field value.

        Returns:
            The validated sort_by field value.

        Raises:
            ValidationError: If the sort_by field is not a string.
            ValueError: If the resource can't be sorted by this field.
        """
        # Somehow pydantic allows you to pass in int values, which will be
        #  interpreted as string, however within the validator they are still
        #  integers, which don't have a .split() method
        if not isinstance(value, str):
            raise ValidationError(
                f"str type expected for the sort_by field. "
                f"Received a {type(value)}"
            )
        column = value
        split_value = value.split(":", 1)
        if len(split_value) == 2:
            column = split_value[1]

            if split_value[0] not in SorterOps.values():
                logger.warning(
                    "Invalid operand used for column sorting. "
                    "Only the following operands are supported `%s`. "
                    "Defaulting to 'asc' on column `%s`.",
                    SorterOps.values(),
                    column,
                )
                value = column

        if column in cls.CUSTOM_SORTING_OPTIONS:
            return value
        elif column in cls.FILTER_EXCLUDE_FIELDS:
            raise ValueError(
                f"This resource can not be sorted by this field: '{value}'"
            )
        if column in cls.model_fields:
            return value
        else:
            raise ValueError(
                "You can only sort by valid fields of this resource"
            )

    @model_validator(mode="before")
    @classmethod
    @before_validator_handler
    def filter_ops(cls, data: Dict[str, Any]) -> Dict[str, Any]:
        """Parse incoming filters to ensure all filters are legal.

        Args:
            data: The values of the class.

        Returns:
            The values of the class.
        """
        cls._generate_filter_list(data)
        return data

    @property
    def list_of_filters(self) -> List[Filter]:
        """Converts the class variables into a list of usable Filter Models.

        Returns:
            A list of Filter models.
        """
        return self._generate_filter_list(
            {key: getattr(self, key) for key in self.model_fields}
        )

    @property
    def sorting_params(self) -> Tuple[str, SorterOps]:
        """Converts the class variables into a list of usable Filter Models.

        Returns:
            A tuple of the column to sort by and the sorting operand.
        """
        column = self.sort_by
        # The default sorting operand is asc
        operator = SorterOps.ASCENDING

        # Check if user explicitly set an operand
        split_value = self.sort_by.split(":", 1)
        if len(split_value) == 2:
            column = split_value[1]
            operator = SorterOps(split_value[0])

        return column, operator

    def configure_rbac(
        self,
        authenticated_user_id: UUID,
        **column_allowed_ids: Optional[Set[UUID]],
    ) -> None:
        """Configure RBAC allowed column values.

        Args:
            authenticated_user_id: ID of the authenticated user. All entities
                owned by this user will be included.
            column_allowed_ids: Set of IDs per column to limit the query to.
                If given, the remaining filters will be applied to entities
                within this set only. If `None`, the remaining filters will
                be applied to all entries in the table.
        """
        self._rbac_configuration = (authenticated_user_id, column_allowed_ids)

    def generate_rbac_filter(
        self,
        table: Type["AnySchema"],
    ) -> Optional["ColumnElement[bool]"]:
        """Generates an optional RBAC filter.

        Args:
            table: The query table.

        Returns:
            The RBAC filter.
        """
        from sqlmodel import or_

        if not self._rbac_configuration:
            return None

        expressions = []

        for column_name, allowed_ids in self._rbac_configuration[1].items():
            if allowed_ids is not None:
                expression = getattr(table, column_name).in_(allowed_ids)
                expressions.append(expression)

        if expressions and hasattr(table, "user_id"):
            # If `expressions` is not empty, we do not have full access to all
            # rows of the table. In this case, we also include rows which the
            # user owns.

            # Unowned entities are considered server-owned and can be seen
            # by anyone
            expressions.append(getattr(table, "user_id").is_(None))
            # The authenticated user owns this entity
            expressions.append(
                getattr(table, "user_id") == self._rbac_configuration[0]
            )

        if expressions:
            return or_(*expressions)
        else:
            return None

    @classmethod
    def _generate_filter_list(cls, values: Dict[str, Any]) -> List[Filter]:
        """Create a list of filters from a (column, value) dictionary.

        Args:
            values: A dictionary of column names and values to filter on.

        Returns:
            A list of filters.
        """
        list_of_filters: List[Filter] = []

        for key, value in values.items():
            # Ignore excluded filters
            if key in cls.FILTER_EXCLUDE_FIELDS:
                continue

            # Skip filtering for None values
            if value is None:
                continue

            # Determine the operator and filter value
            value, operator = cls._resolve_operator(value)

            # Define the filter
            filter = FilterGenerator(cls).define_filter(
                column=key, value=value, operator=operator
            )
            list_of_filters.append(filter)

        return list_of_filters

    @staticmethod
    def _resolve_operator(value: Any) -> Tuple[Any, GenericFilterOps]:
        """Determine the operator and filter value from a user-provided value.

        If the user-provided value is a string of the form "operator:value",
        then the operator is extracted and the value is returned. Otherwise,
        `GenericFilterOps.EQUALS` is used as default operator and the value
        is returned as-is.

        Args:
            value: The user-provided value.

        Returns:
            A tuple of the filter value and the operator.

        Raises:
            ValueError: when we try to use the `oneof` operator with the wrong
                value.
        """
        operator = GenericFilterOps.EQUALS  # Default operator
        if isinstance(value, str):
            split_value = value.split(":", 1)
            if (
                len(split_value) == 2
                and split_value[0] in GenericFilterOps.values()
            ):
                value = split_value[1]
                operator = GenericFilterOps(split_value[0])

            if operator == operator.ONEOF:
                try:
                    value = json.loads(value)
                    if not isinstance(value, list):
                        raise ValueError
                except ValueError:
                    raise ValueError(ONEOF_ERROR)

        return value, operator

    def generate_name_or_id_query_conditions(
        self,
        value: Union[UUID, str],
        table: Type["NamedSchema"],
        additional_columns: Optional[List[str]] = None,
    ) -> "ColumnElement[bool]":
        """Generate filter conditions for name or id of a table.

        Args:
            value: The filter value.
            table: The table to filter.
            additional_columns: Additional table columns that should also
                filtered for the given value as part of the or condition.

        Returns:
            The query conditions.
        """
        from sqlmodel import or_

        value, operator = BaseFilter._resolve_operator(value)
        value = str(value)

        conditions = []

        filter_ = FilterGenerator(table).define_filter(
            column="id", value=value, operator=operator
        )
        conditions.append(filter_.generate_query_conditions(table=table))

        filter_ = FilterGenerator(table).define_filter(
            column="name", value=value, operator=operator
        )
        conditions.append(filter_.generate_query_conditions(table=table))

        for column in additional_columns or []:
            filter_ = FilterGenerator(table).define_filter(
                column=column, value=value, operator=operator
            )
            conditions.append(filter_.generate_query_conditions(table=table))

        return or_(*conditions)

    @staticmethod
    def generate_custom_query_conditions_for_column(
        value: Any,
        table: Type[SQLModel],
        column: str,
        json_encode_value: bool = False,
    ) -> "ColumnElement[bool]":
        """Generate custom filter conditions for a column of a table.

        Args:
            value: The filter value.
            table: The table which contains the column.
            column: The column name.
            json_encode_value: Whether to json encode the value.

        Returns:
            The query conditions.
        """
        value, operator = BaseFilter._resolve_operator(value)
        filter_ = FilterGenerator(table).define_filter(
            column=column, value=value, operator=operator
        )
        if isinstance(filter_, StrFilter):
            filter_.json_encode_value = json_encode_value

        return filter_.generate_query_conditions(table=table)

    @property
    def offset(self) -> int:
        """Returns the offset needed for the query on the data persistence layer.

        Returns:
            The offset for the query.
        """
        return self.size * (self.page - 1)

    def generate_filter(
        self, table: Type["AnySchema"]
    ) -> Union["ColumnElement[bool]"]:
        """Generate the filter for the query.

        Args:
            table: The Table that is being queried from.

        Returns:
            The filter expression for the query.

        Raises:
            RuntimeError: If a valid logical operator is not supplied.
        """
        from sqlmodel import and_, or_

        filters = []
        for column_filter in self.list_of_filters:
            filters.append(
                column_filter.generate_query_conditions(table=table)
            )
        for custom_filter in self.get_custom_filters(table):
            filters.append(custom_filter)
        if self.logical_operator == LogicalOperators.OR:
            return or_(False, *filters)
        elif self.logical_operator == LogicalOperators.AND:
            return and_(True, *filters)
        else:
            raise RuntimeError("No valid logical operator was supplied.")

    def get_custom_filters(
        self, table: Type["AnySchema"]
    ) -> List["ColumnElement[bool]"]:
        """Get custom filters.

        This can be overridden by subclasses to define custom filters that are
        not based on the columns of the underlying table.

        Args:
            table: The query table.

        Returns:
            A list of custom filters.
        """
        return []

    def apply_filter(
        self,
        query: AnyQuery,
        table: Type["AnySchema"],
    ) -> AnyQuery:
        """Applies the filter to a query.

        Args:
            query: The query to which to apply the filter.
            table: The query table.

        Returns:
            The query with filter applied.
        """
        rbac_filter = self.generate_rbac_filter(table=table)

        if rbac_filter is not None:
            query = query.where(rbac_filter)

        filters = self.generate_filter(table=table)

        if filters is not None:
            query = query.where(filters)

        return query

    def apply_sorting(
        self,
        query: AnyQuery,
        table: Type["AnySchema"],
    ) -> AnyQuery:
        """Apply sorting to the query.

        Args:
            query: The query to which to apply the sorting.
            table: The query table.

        Returns:
            The query with sorting applied.
        """
        column, operand = self.sorting_params

        if operand == SorterOps.DESCENDING:
            sort_clause = desc(getattr(table, column))  # type: ignore[var-annotated]
        else:
            sort_clause = asc(getattr(table, column))

        # We always add the `id` column as a tiebreaker to ensure a stable,
        # repeatable order of items, otherwise subsequent pages might contain
        # the same items.
        query = query.order_by(sort_clause, asc(table.id))  # type: ignore[arg-type]

        return query

`list_of_filters` `property`

Converts the class variables into a list of usable Filter Models.

Returns:

Type	Description
`List[Filter]`	A list of Filter models.

`offset` `property`

Returns the offset needed for the query on the data persistence layer.

Returns:

Type	Description
`int`	The offset for the query.

`sorting_params` `property`

Converts the class variables into a list of usable Filter Models.

Returns:

Type	Description
`Tuple[str, SorterOps]`	A tuple of the column to sort by and the sorting operand.

`apply_filter(query, table)`

Applies the filter to a query.

Parameters:

Name	Type	Description	Default
`query`	`AnyQuery`	The query to which to apply the filter.	required
`table`	`Type[AnySchema]`	The query table.	required

Returns:

Type	Description
`AnyQuery`	The query with filter applied.

Source code in src/zenml/models/v2/base/filter.py

def apply_filter(
    self,
    query: AnyQuery,
    table: Type["AnySchema"],
) -> AnyQuery:
    """Applies the filter to a query.

    Args:
        query: The query to which to apply the filter.
        table: The query table.

    Returns:
        The query with filter applied.
    """
    rbac_filter = self.generate_rbac_filter(table=table)

    if rbac_filter is not None:
        query = query.where(rbac_filter)

    filters = self.generate_filter(table=table)

    if filters is not None:
        query = query.where(filters)

    return query

`apply_sorting(query, table)`

Apply sorting to the query.

Parameters:

Name	Type	Description	Default
`query`	`AnyQuery`	The query to which to apply the sorting.	required
`table`	`Type[AnySchema]`	The query table.	required

Returns:

Type	Description
`AnyQuery`	The query with sorting applied.

Source code in src/zenml/models/v2/base/filter.py

def apply_sorting(
    self,
    query: AnyQuery,
    table: Type["AnySchema"],
) -> AnyQuery:
    """Apply sorting to the query.

    Args:
        query: The query to which to apply the sorting.
        table: The query table.

    Returns:
        The query with sorting applied.
    """
    column, operand = self.sorting_params

    if operand == SorterOps.DESCENDING:
        sort_clause = desc(getattr(table, column))  # type: ignore[var-annotated]
    else:
        sort_clause = asc(getattr(table, column))

    # We always add the `id` column as a tiebreaker to ensure a stable,
    # repeatable order of items, otherwise subsequent pages might contain
    # the same items.
    query = query.order_by(sort_clause, asc(table.id))  # type: ignore[arg-type]

    return query

`configure_rbac(authenticated_user_id, **column_allowed_ids)`

Configure RBAC allowed column values.

Parameters:

Name	Type	Description	Default
`authenticated_user_id`	`UUID`	ID of the authenticated user. All entities owned by this user will be included.	required
`column_allowed_ids`	`Optional[Set[UUID]]`	Set of IDs per column to limit the query to. If given, the remaining filters will be applied to entities within this set only. If `None`, the remaining filters will be applied to all entries in the table.	`{}`

Source code in src/zenml/models/v2/base/filter.py

def configure_rbac(
    self,
    authenticated_user_id: UUID,
    **column_allowed_ids: Optional[Set[UUID]],
) -> None:
    """Configure RBAC allowed column values.

    Args:
        authenticated_user_id: ID of the authenticated user. All entities
            owned by this user will be included.
        column_allowed_ids: Set of IDs per column to limit the query to.
            If given, the remaining filters will be applied to entities
            within this set only. If `None`, the remaining filters will
            be applied to all entries in the table.
    """
    self._rbac_configuration = (authenticated_user_id, column_allowed_ids)

`filter_ops(data)` `classmethod`

Parse incoming filters to ensure all filters are legal.

Parameters:

Name	Type	Description	Default
`data`	`Dict[str, Any]`	The values of the class.	required

Returns:

Type	Description
`Dict[str, Any]`	The values of the class.

Source code in src/zenml/models/v2/base/filter.py

@model_validator(mode="before")
@classmethod
@before_validator_handler
def filter_ops(cls, data: Dict[str, Any]) -> Dict[str, Any]:
    """Parse incoming filters to ensure all filters are legal.

    Args:
        data: The values of the class.

    Returns:
        The values of the class.
    """
    cls._generate_filter_list(data)
    return data

`generate_custom_query_conditions_for_column(value, table, column, json_encode_value=False)` `staticmethod`

Generate custom filter conditions for a column of a table.

Parameters:

Name	Type	Description	Default
`value`	`Any`	The filter value.	required
`table`	`Type[SQLModel]`	The table which contains the column.	required
`column`	`str`	The column name.	required
`json_encode_value`	`bool`	Whether to json encode the value.	`False`

Returns:

Type	Description
`ColumnElement[bool]`	The query conditions.

Source code in src/zenml/models/v2/base/filter.py

@staticmethod
def generate_custom_query_conditions_for_column(
    value: Any,
    table: Type[SQLModel],
    column: str,
    json_encode_value: bool = False,
) -> "ColumnElement[bool]":
    """Generate custom filter conditions for a column of a table.

    Args:
        value: The filter value.
        table: The table which contains the column.
        column: The column name.
        json_encode_value: Whether to json encode the value.

    Returns:
        The query conditions.
    """
    value, operator = BaseFilter._resolve_operator(value)
    filter_ = FilterGenerator(table).define_filter(
        column=column, value=value, operator=operator
    )
    if isinstance(filter_, StrFilter):
        filter_.json_encode_value = json_encode_value

    return filter_.generate_query_conditions(table=table)

`generate_filter(table)`

Generate the filter for the query.

Parameters:

Name	Type	Description	Default
`table`	`Type[AnySchema]`	The Table that is being queried from.	required

Returns:

Type	Description
`Union[ColumnElement[bool]]`	The filter expression for the query.

Raises:

Type	Description
`RuntimeError`	If a valid logical operator is not supplied.

Source code in src/zenml/models/v2/base/filter.py

def generate_filter(
    self, table: Type["AnySchema"]
) -> Union["ColumnElement[bool]"]:
    """Generate the filter for the query.

    Args:
        table: The Table that is being queried from.

    Returns:
        The filter expression for the query.

    Raises:
        RuntimeError: If a valid logical operator is not supplied.
    """
    from sqlmodel import and_, or_

    filters = []
    for column_filter in self.list_of_filters:
        filters.append(
            column_filter.generate_query_conditions(table=table)
        )
    for custom_filter in self.get_custom_filters(table):
        filters.append(custom_filter)
    if self.logical_operator == LogicalOperators.OR:
        return or_(False, *filters)
    elif self.logical_operator == LogicalOperators.AND:
        return and_(True, *filters)
    else:
        raise RuntimeError("No valid logical operator was supplied.")

`generate_name_or_id_query_conditions(value, table, additional_columns=None)`

Generate filter conditions for name or id of a table.

Parameters:

Name	Type	Description	Default
`value`	`Union[UUID, str]`	The filter value.	required
`table`	`Type[NamedSchema]`	The table to filter.	required
`additional_columns`	`Optional[List[str]]`	Additional table columns that should also filtered for the given value as part of the or condition.	`None`

Returns:

Type	Description
`ColumnElement[bool]`	The query conditions.

Source code in src/zenml/models/v2/base/filter.py

def generate_name_or_id_query_conditions(
    self,
    value: Union[UUID, str],
    table: Type["NamedSchema"],
    additional_columns: Optional[List[str]] = None,
) -> "ColumnElement[bool]":
    """Generate filter conditions for name or id of a table.

    Args:
        value: The filter value.
        table: The table to filter.
        additional_columns: Additional table columns that should also
            filtered for the given value as part of the or condition.

    Returns:
        The query conditions.
    """
    from sqlmodel import or_

    value, operator = BaseFilter._resolve_operator(value)
    value = str(value)

    conditions = []

    filter_ = FilterGenerator(table).define_filter(
        column="id", value=value, operator=operator
    )
    conditions.append(filter_.generate_query_conditions(table=table))

    filter_ = FilterGenerator(table).define_filter(
        column="name", value=value, operator=operator
    )
    conditions.append(filter_.generate_query_conditions(table=table))

    for column in additional_columns or []:
        filter_ = FilterGenerator(table).define_filter(
            column=column, value=value, operator=operator
        )
        conditions.append(filter_.generate_query_conditions(table=table))

    return or_(*conditions)

`generate_rbac_filter(table)`

Generates an optional RBAC filter.

Parameters:

Name	Type	Description	Default
`table`	`Type[AnySchema]`	The query table.	required

Returns:

Type	Description
`Optional[ColumnElement[bool]]`	The RBAC filter.

Source code in src/zenml/models/v2/base/filter.py

def generate_rbac_filter(
    self,
    table: Type["AnySchema"],
) -> Optional["ColumnElement[bool]"]:
    """Generates an optional RBAC filter.

    Args:
        table: The query table.

    Returns:
        The RBAC filter.
    """
    from sqlmodel import or_

    if not self._rbac_configuration:
        return None

    expressions = []

    for column_name, allowed_ids in self._rbac_configuration[1].items():
        if allowed_ids is not None:
            expression = getattr(table, column_name).in_(allowed_ids)
            expressions.append(expression)

    if expressions and hasattr(table, "user_id"):
        # If `expressions` is not empty, we do not have full access to all
        # rows of the table. In this case, we also include rows which the
        # user owns.

        # Unowned entities are considered server-owned and can be seen
        # by anyone
        expressions.append(getattr(table, "user_id").is_(None))
        # The authenticated user owns this entity
        expressions.append(
            getattr(table, "user_id") == self._rbac_configuration[0]
        )

    if expressions:
        return or_(*expressions)
    else:
        return None

`get_custom_filters(table)`

Get custom filters.

This can be overridden by subclasses to define custom filters that are not based on the columns of the underlying table.

Parameters:

Name	Type	Description	Default
`table`	`Type[AnySchema]`	The query table.	required

Returns:

Type	Description
`List[ColumnElement[bool]]`	A list of custom filters.

Source code in src/zenml/models/v2/base/filter.py

def get_custom_filters(
    self, table: Type["AnySchema"]
) -> List["ColumnElement[bool]"]:
    """Get custom filters.

    This can be overridden by subclasses to define custom filters that are
    not based on the columns of the underlying table.

    Args:
        table: The query table.

    Returns:
        A list of custom filters.
    """
    return []

`validate_sort_by(value)` `classmethod`

Validate that the sort_column is a valid column with a valid operand.

Parameters:

Name	Type	Description	Default
`value`	`Any`	The sort_by field value.	required

Returns:

Type	Description
`Any`	The validated sort_by field value.

Raises:

Type	Description
`ValidationError`	If the sort_by field is not a string.
`ValueError`	If the resource can't be sorted by this field.

Source code in src/zenml/models/v2/base/filter.py

@field_validator("sort_by", mode="before")
@classmethod
def validate_sort_by(cls, value: Any) -> Any:
    """Validate that the sort_column is a valid column with a valid operand.

    Args:
        value: The sort_by field value.

    Returns:
        The validated sort_by field value.

    Raises:
        ValidationError: If the sort_by field is not a string.
        ValueError: If the resource can't be sorted by this field.
    """
    # Somehow pydantic allows you to pass in int values, which will be
    #  interpreted as string, however within the validator they are still
    #  integers, which don't have a .split() method
    if not isinstance(value, str):
        raise ValidationError(
            f"str type expected for the sort_by field. "
            f"Received a {type(value)}"
        )
    column = value
    split_value = value.split(":", 1)
    if len(split_value) == 2:
        column = split_value[1]

        if split_value[0] not in SorterOps.values():
            logger.warning(
                "Invalid operand used for column sorting. "
                "Only the following operands are supported `%s`. "
                "Defaulting to 'asc' on column `%s`.",
                SorterOps.values(),
                column,
            )
            value = column

    if column in cls.CUSTOM_SORTING_OPTIONS:
        return value
    elif column in cls.FILTER_EXCLUDE_FIELDS:
        raise ValueError(
            f"This resource can not be sorted by this field: '{value}'"
        )
    if column in cls.model_fields:
        return value
    else:
        raise ValueError(
            "You can only sort by valid fields of this resource"
        )

`BaseIdentifiedResponse`

Bases: BaseResponse[AnyDatedBody, AnyMetadata, AnyResources], Generic[AnyDatedBody, AnyMetadata, AnyResources]

Base domain model for resources with DB representation.

Source code in src/zenml/models/v2/base/base.py

class BaseIdentifiedResponse(
    BaseResponse[AnyDatedBody, AnyMetadata, AnyResources],
    Generic[AnyDatedBody, AnyMetadata, AnyResources],
):
    """Base domain model for resources with DB representation."""

    id: UUID = Field(title="The unique resource id.")

    permission_denied: bool = False

    # Helper functions
    def __hash__(self) -> int:
        """Implementation of hash magic method.

        Returns:
            Hash of the UUID.
        """
        return hash((type(self),) + tuple([self.id]))

    def __eq__(self, other: Any) -> bool:
        """Implementation of equality magic method.

        Args:
            other: The other object to compare to.

        Returns:
            True if the other object is of the same type and has the same UUID.
        """
        if isinstance(other, type(self)):
            return self.id == other.id
        else:
            return False

    def _validate_hydrated_version(
        self,
        hydrated_model: "BaseResponse[AnyDatedBody, AnyMetadata, AnyResources]",
    ) -> None:
        """Helper method to validate the values within the hydrated version.

        Args:
            hydrated_model: the hydrated version of the model.

        Raises:
            HydrationError: if the hydrated version has different values set
                for either the name of the body fields and the
                _method_body_mutation is set to ResponseBodyUpdate.DENY.
        """
        super()._validate_hydrated_version(hydrated_model)

        assert isinstance(hydrated_model, type(self))

        # Check if the ID is the same
        if self.id != hydrated_model.id:
            raise HydrationError(
                "The hydrated version of the model does not have the same id."
            )

    def get_hydrated_version(
        self,
    ) -> "BaseIdentifiedResponse[AnyDatedBody, AnyMetadata, AnyResources]":
        """Abstract method to fetch the hydrated version of the model.

        Raises:
            NotImplementedError: in case the method is not implemented.
        """
        raise NotImplementedError(
            "Please implement a `get_hydrated_version` method before "
            "using/hydrating the model."
        )

    def get_body(self) -> "AnyDatedBody":
        """Fetch the body of the entity.

        Returns:
            The body field of the response.

        Raises:
            IllegalOperationError: If the user lacks permission to access the
                entity represented by this response.
        """
        if self.permission_denied:
            raise IllegalOperationError(
                f"Missing permissions to access {type(self).__name__} with "
                f"ID {self.id}."
            )

        return super().get_body()

    def get_metadata(self) -> "AnyMetadata":
        """Fetch the metadata of the entity.

        Returns:
            The metadata field of the response.

        Raises:
            IllegalOperationError: If the user lacks permission to access this
                entity represented by this response.
        """
        if self.permission_denied:
            raise IllegalOperationError(
                f"Missing permissions to access {type(self).__name__} with "
                f"ID {self.id}."
            )

        return super().get_metadata()

    # Analytics
    def get_analytics_metadata(self) -> Dict[str, Any]:
        """Fetches the analytics metadata for base response models.

        Returns:
            The analytics metadata.
        """
        metadata = super().get_analytics_metadata()
        metadata["entity_id"] = self.id
        return metadata

    # Body and metadata properties
    @property
    def created(self) -> datetime:
        """The `created` property.

        Returns:
            the value of the property.
        """
        return self.get_body().created

    @property
    def updated(self) -> datetime:
        """The `updated` property.

        Returns:
            the value of the property.
        """
        return self.get_body().updated

`created` `property`

The created property.

Returns:

Type	Description
`datetime`	the value of the property.

`updated` `property`

The updated property.

Returns:

Type	Description
`datetime`	the value of the property.

`eq(other)`

Implementation of equality magic method.

Parameters:

Name	Type	Description	Default
`other`	`Any`	The other object to compare to.	required

Returns:

Type	Description
`bool`	True if the other object is of the same type and has the same UUID.

Source code in src/zenml/models/v2/base/base.py

def __eq__(self, other: Any) -> bool:
    """Implementation of equality magic method.

    Args:
        other: The other object to compare to.

    Returns:
        True if the other object is of the same type and has the same UUID.
    """
    if isinstance(other, type(self)):
        return self.id == other.id
    else:
        return False

`hash()`

Implementation of hash magic method.

Returns:

Type	Description
`int`	Hash of the UUID.

Source code in src/zenml/models/v2/base/base.py

def __hash__(self) -> int:
    """Implementation of hash magic method.

    Returns:
        Hash of the UUID.
    """
    return hash((type(self),) + tuple([self.id]))

`get_analytics_metadata()`

Fetches the analytics metadata for base response models.

Returns:

Type	Description
`Dict[str, Any]`	The analytics metadata.

Source code in src/zenml/models/v2/base/base.py

def get_analytics_metadata(self) -> Dict[str, Any]:
    """Fetches the analytics metadata for base response models.

    Returns:
        The analytics metadata.
    """
    metadata = super().get_analytics_metadata()
    metadata["entity_id"] = self.id
    return metadata

`get_body()`

Fetch the body of the entity.

Returns:

Type	Description
`AnyDatedBody`	The body field of the response.

Raises:

Type	Description
`IllegalOperationError`	If the user lacks permission to access the entity represented by this response.

Source code in src/zenml/models/v2/base/base.py

def get_body(self) -> "AnyDatedBody":
    """Fetch the body of the entity.

    Returns:
        The body field of the response.

    Raises:
        IllegalOperationError: If the user lacks permission to access the
            entity represented by this response.
    """
    if self.permission_denied:
        raise IllegalOperationError(
            f"Missing permissions to access {type(self).__name__} with "
            f"ID {self.id}."
        )

    return super().get_body()

`get_hydrated_version()`

Abstract method to fetch the hydrated version of the model.

Raises:

Type	Description
`NotImplementedError`	in case the method is not implemented.

Source code in src/zenml/models/v2/base/base.py

def get_hydrated_version(
    self,
) -> "BaseIdentifiedResponse[AnyDatedBody, AnyMetadata, AnyResources]":
    """Abstract method to fetch the hydrated version of the model.

    Raises:
        NotImplementedError: in case the method is not implemented.
    """
    raise NotImplementedError(
        "Please implement a `get_hydrated_version` method before "
        "using/hydrating the model."
    )

`get_metadata()`

Fetch the metadata of the entity.

Returns:

Type	Description
`AnyMetadata`	The metadata field of the response.

Raises:

Type	Description
`IllegalOperationError`	If the user lacks permission to access this entity represented by this response.

Source code in src/zenml/models/v2/base/base.py

def get_metadata(self) -> "AnyMetadata":
    """Fetch the metadata of the entity.

    Returns:
        The metadata field of the response.

    Raises:
        IllegalOperationError: If the user lacks permission to access this
            entity represented by this response.
    """
    if self.permission_denied:
        raise IllegalOperationError(
            f"Missing permissions to access {type(self).__name__} with "
            f"ID {self.id}."
        )

    return super().get_metadata()

`BasePluginFlavorResponse`

Bases: BaseResponse[AnyPluginBody, AnyPluginMetadata, AnyPluginResources], Generic[AnyPluginBody, AnyPluginMetadata, AnyPluginResources]

Base response for all Plugin Flavors.

Source code in src/zenml/models/v2/base/base_plugin_flavor.py

class BasePluginFlavorResponse(
    BaseResponse[AnyPluginBody, AnyPluginMetadata, AnyPluginResources],
    Generic[AnyPluginBody, AnyPluginMetadata, AnyPluginResources],
):
    """Base response for all Plugin Flavors."""

    name: str = Field(title="Name of the flavor.")
    type: PluginType = Field(title="Type of the plugin.")
    subtype: PluginSubType = Field(title="Subtype of the plugin.")
    model_config = ConfigDict(extra="ignore")

    def get_hydrated_version(
        self,
    ) -> "BasePluginFlavorResponse[AnyPluginBody, AnyPluginMetadata, AnyPluginResources]":
        """Abstract method to fetch the hydrated version of the model.

        Returns:
            Hydrated version of the PluginFlavorResponse
        """
        # TODO: shouldn't this call the Zen store ? The client should not have
        #  to know about the plugin flavor registry
        from zenml.zen_server.utils import plugin_flavor_registry

        plugin_flavor = plugin_flavor_registry().get_flavor_class(
            name=self.name, _type=self.type, subtype=self.subtype
        )
        return plugin_flavor.get_flavor_response_model(hydrate=True)

`get_hydrated_version()`

Abstract method to fetch the hydrated version of the model.

Returns:

Type	Description
`BasePluginFlavorResponse[AnyPluginBody, AnyPluginMetadata, AnyPluginResources]`	Hydrated version of the PluginFlavorResponse

Source code in src/zenml/models/v2/base/base_plugin_flavor.py

def get_hydrated_version(
    self,
) -> "BasePluginFlavorResponse[AnyPluginBody, AnyPluginMetadata, AnyPluginResources]":
    """Abstract method to fetch the hydrated version of the model.

    Returns:
        Hydrated version of the PluginFlavorResponse
    """
    # TODO: shouldn't this call the Zen store ? The client should not have
    #  to know about the plugin flavor registry
    from zenml.zen_server.utils import plugin_flavor_registry

    plugin_flavor = plugin_flavor_registry().get_flavor_class(
        name=self.name, _type=self.type, subtype=self.subtype
    )
    return plugin_flavor.get_flavor_response_model(hydrate=True)

`BaseRequest`

Bases: BaseZenModel

Base request model.

Used as a base class for all request models.

Source code in src/zenml/models/v2/base/base.py

class BaseRequest(BaseZenModel):
    """Base request model.

    Used as a base class for all request models.
    """

`BaseResponse`

Bases: BaseZenModel, Generic[AnyBody, AnyMetadata, AnyResources]

Base domain model for all responses.

Source code in src/zenml/models/v2/base/base.py

class BaseResponse(BaseZenModel, Generic[AnyBody, AnyMetadata, AnyResources]):
    """Base domain model for all responses."""

    # Body and metadata pair
    body: Optional["AnyBody"] = Field(
        default=None, title="The body of the resource."
    )
    metadata: Optional["AnyMetadata"] = Field(
        default=None, title="The metadata related to this resource."
    )
    resources: Optional["AnyResources"] = Field(
        default=None, title="The resources related to this resource."
    )

    _response_update_strategy: ResponseUpdateStrategy = (
        ResponseUpdateStrategy.ALLOW
    )
    _warn_on_response_updates: bool = True

    def _validate_hydrated_version(
        self,
        hydrated_model: "BaseResponse[AnyBody, AnyMetadata, AnyResources]",
    ) -> None:
        """Helper method to validate the values within the hydrated version.

        Args:
            hydrated_model: the hydrated version of the model.

        Raises:
            HydrationError: if the hydrated version has different values set
                for either the name of the body fields and the
                _method_body_mutation is set to ResponseBodyUpdate.DENY.
        """
        # Check whether the metadata exists in the hydrated version
        if hydrated_model.metadata is None:
            raise HydrationError(
                "The hydrated model does not have a metadata field."
            )

        # Check if the name has changed
        if "name" in self.model_fields:
            original_name = getattr(self, "name")
            hydrated_name = getattr(hydrated_model, "name")

            if original_name != hydrated_name:
                if (
                    self._response_update_strategy
                    == ResponseUpdateStrategy.ALLOW
                ):
                    setattr(self, "name", hydrated_name)

                    if self._warn_on_response_updates:
                        logger.warning(
                            f"The name of the entity has changed from "
                            f"`{original_name}` to `{hydrated_name}`."
                        )

                elif (
                    self._response_update_strategy
                    == ResponseUpdateStrategy.IGNORE
                ):
                    if self._warn_on_response_updates:
                        logger.warning(
                            f"Ignoring the name change in the hydrated version "
                            f"of the response: `{original_name}` to "
                            f"`{hydrated_name}`."
                        )
                elif (
                    self._response_update_strategy
                    == ResponseUpdateStrategy.DENY
                ):
                    raise HydrationError(
                        f"Failing the hydration, because there is a change in "
                        f"the name of the entity: `{original_name}` to "
                        f"`{hydrated_name}`."
                    )

        # Check all the fields in the body
        for field in self.get_body().model_fields:
            original_value = getattr(self.get_body(), field)
            hydrated_value = getattr(hydrated_model.get_body(), field)

            if original_value != hydrated_value:
                if (
                    self._response_update_strategy
                    == ResponseUpdateStrategy.ALLOW
                ):
                    setattr(self.get_body(), field, hydrated_value)

                    if self._warn_on_response_updates:
                        logger.warning(
                            f"The field `{field}` in the body of the response "
                            f"has changed from `{original_value}` to "
                            f"`{hydrated_value}`."
                        )

                elif (
                    self._response_update_strategy
                    == ResponseUpdateStrategy.IGNORE
                ):
                    if self._warn_on_response_updates:
                        logger.warning(
                            f"Ignoring the change in the hydrated version of "
                            f"the field `{field}`: `{original_value}` -> "
                            f"`{hydrated_value}`."
                        )
                elif (
                    self._response_update_strategy
                    == ResponseUpdateStrategy.DENY
                ):
                    raise HydrationError(
                        f"Failing the hydration, because there is a change in "
                        f"the field `{field}`: `{original_value}` -> "
                        f"`{hydrated_value}`"
                    )

    def hydrate(self) -> None:
        """Hydrate the response."""
        hydrated_version = self.get_hydrated_version()
        self._validate_hydrated_version(hydrated_version)

        self.resources = hydrated_version.resources
        self.metadata = hydrated_version.metadata

    def get_hydrated_version(
        self,
    ) -> "BaseResponse[AnyBody, AnyMetadata, AnyResources]":
        """Abstract method to fetch the hydrated version of the model.

        Raises:
            NotImplementedError: in case the method is not implemented.
        """
        raise NotImplementedError(
            "Please implement a `get_hydrated_version` method before "
            "using/hydrating the model."
        )

    def get_body(self) -> "AnyBody":
        """Fetch the body of the entity.

        Returns:
            The body field of the response.

        Raises:
            RuntimeError: If the body was not included in the response.
        """
        if not self.body:
            raise RuntimeError(
                f"Missing response body for {type(self).__name__}."
            )

        return self.body

    def get_metadata(self) -> "AnyMetadata":
        """Fetch the metadata of the entity.

        Returns:
            The metadata field of the response.
        """
        if self.metadata is None:
            # If the metadata is not there, check the class first.
            metadata_annotation = self.model_fields["metadata"].annotation
            assert metadata_annotation is not None, (
                "For each response model, an annotated metadata"
                "field should exist."
            )

            # metadata is defined as:
            #   metadata: Optional[....ResponseMetadata] = Field(default=None)
            # We need to find the actual class inside the Optional annotation.
            from zenml.utils.typing_utils import get_args

            metadata_type = get_args(metadata_annotation)[0]
            assert issubclass(metadata_type, BaseResponseMetadata)

            if len(metadata_type.model_fields):
                # If the metadata class defines any fields, fetch the metadata
                # through the hydrated version.
                self.hydrate()
            else:
                # Otherwise, use the metadata class to create an empty metadata
                # object.
                self.metadata = metadata_type()

        assert self.metadata is not None

        return self.metadata

    def get_resources(self) -> "AnyResources":
        """Fetch the resources related to this entity.

        Returns:
            The resources field of the response.

        Raises:
            RuntimeError: If the resources field was not included in the response.
        """
        if self.resources is None:
            # If the resources are not there, check the class first.
            resources_annotation = self.model_fields["resources"].annotation
            assert resources_annotation is not None, (
                "For each response model, an annotated resources"
                "field should exist."
            )

            # resources is defined as:
            #   resources: Optional[....ResponseResources] = Field(default=None)
            # We need to find the actual class inside the Optional annotation.
            from zenml.utils.typing_utils import get_args

            resources_type = get_args(resources_annotation)[0]
            assert issubclass(resources_type, BaseResponseResources)

            if len(resources_type.model_fields):
                # If the resources class defines any fields, fetch the resources
                # through the hydrated version.
                self.hydrate()
            else:
                # Otherwise, use the resources class to create an empty
                # resources object.
                self.resources = resources_type()

        if self.resources is None:
            raise RuntimeError(
                f"Missing response resources for {type(self).__name__}."
            )

        return self.resources

`get_body()`

Fetch the body of the entity.

Returns:

Type	Description
`AnyBody`	The body field of the response.

Raises:

Type	Description
`RuntimeError`	If the body was not included in the response.

Source code in src/zenml/models/v2/base/base.py

def get_body(self) -> "AnyBody":
    """Fetch the body of the entity.

    Returns:
        The body field of the response.

    Raises:
        RuntimeError: If the body was not included in the response.
    """
    if not self.body:
        raise RuntimeError(
            f"Missing response body for {type(self).__name__}."
        )

    return self.body

`get_hydrated_version()`

Abstract method to fetch the hydrated version of the model.

Raises:

Type	Description
`NotImplementedError`	in case the method is not implemented.

Source code in src/zenml/models/v2/base/base.py

def get_hydrated_version(
    self,
) -> "BaseResponse[AnyBody, AnyMetadata, AnyResources]":
    """Abstract method to fetch the hydrated version of the model.

    Raises:
        NotImplementedError: in case the method is not implemented.
    """
    raise NotImplementedError(
        "Please implement a `get_hydrated_version` method before "
        "using/hydrating the model."
    )

`get_metadata()`

Fetch the metadata of the entity.

Returns:

Type	Description
`AnyMetadata`	The metadata field of the response.

Source code in src/zenml/models/v2/base/base.py

def get_metadata(self) -> "AnyMetadata":
    """Fetch the metadata of the entity.

    Returns:
        The metadata field of the response.
    """
    if self.metadata is None:
        # If the metadata is not there, check the class first.
        metadata_annotation = self.model_fields["metadata"].annotation
        assert metadata_annotation is not None, (
            "For each response model, an annotated metadata"
            "field should exist."
        )

        # metadata is defined as:
        #   metadata: Optional[....ResponseMetadata] = Field(default=None)
        # We need to find the actual class inside the Optional annotation.
        from zenml.utils.typing_utils import get_args

        metadata_type = get_args(metadata_annotation)[0]
        assert issubclass(metadata_type, BaseResponseMetadata)

        if len(metadata_type.model_fields):
            # If the metadata class defines any fields, fetch the metadata
            # through the hydrated version.
            self.hydrate()
        else:
            # Otherwise, use the metadata class to create an empty metadata
            # object.
            self.metadata = metadata_type()

    assert self.metadata is not None

    return self.metadata

`get_resources()`

Fetch the resources related to this entity.

Returns:

Type	Description
`AnyResources`	The resources field of the response.

Raises:

Type	Description
`RuntimeError`	If the resources field was not included in the response.

Source code in src/zenml/models/v2/base/base.py

def get_resources(self) -> "AnyResources":
    """Fetch the resources related to this entity.

    Returns:
        The resources field of the response.

    Raises:
        RuntimeError: If the resources field was not included in the response.
    """
    if self.resources is None:
        # If the resources are not there, check the class first.
        resources_annotation = self.model_fields["resources"].annotation
        assert resources_annotation is not None, (
            "For each response model, an annotated resources"
            "field should exist."
        )

        # resources is defined as:
        #   resources: Optional[....ResponseResources] = Field(default=None)
        # We need to find the actual class inside the Optional annotation.
        from zenml.utils.typing_utils import get_args

        resources_type = get_args(resources_annotation)[0]
        assert issubclass(resources_type, BaseResponseResources)

        if len(resources_type.model_fields):
            # If the resources class defines any fields, fetch the resources
            # through the hydrated version.
            self.hydrate()
        else:
            # Otherwise, use the resources class to create an empty
            # resources object.
            self.resources = resources_type()

    if self.resources is None:
        raise RuntimeError(
            f"Missing response resources for {type(self).__name__}."
        )

    return self.resources

`hydrate()`

Hydrate the response.

Source code in src/zenml/models/v2/base/base.py

def hydrate(self) -> None:
    """Hydrate the response."""
    hydrated_version = self.get_hydrated_version()
    self._validate_hydrated_version(hydrated_version)

    self.resources = hydrated_version.resources
    self.metadata = hydrated_version.metadata

`BaseResponseBody`

Bases: BaseZenModel

Base body model.

Source code in src/zenml/models/v2/base/base.py

class BaseResponseBody(BaseZenModel):
    """Base body model."""

`BaseResponseMetadata`

Bases: BaseZenModel

Base metadata model.

Used as a base class for all metadata models associated with responses.

Source code in src/zenml/models/v2/base/base.py

class BaseResponseMetadata(BaseZenModel):
    """Base metadata model.

    Used as a base class for all metadata models associated with responses.
    """

`BaseResponseResources`

Bases: BaseZenModel

Base resources model.

Used as a base class for all resource models associated with responses.

Source code in src/zenml/models/v2/base/base.py

class BaseResponseResources(BaseZenModel):
    """Base resources model.

    Used as a base class for all resource models associated with responses.
    """

    model_config = ConfigDict(extra="allow")

`BaseUpdate`

Bases: BaseZenModel

Base update model.

Used as a base class for all update models.

Source code in src/zenml/models/v2/base/base.py

class BaseUpdate(BaseZenModel):
    """Base update model.

    Used as a base class for all update models.
    """

    model_config = ConfigDict(
        # Ignore extras on all update models.
        extra="ignore",
    )

`BaseZenModel`

Bases: YAMLSerializationMixin, AnalyticsTrackedModelMixin

Base model class for all ZenML models.

This class is used as a base class for all ZenML models. It provides functionality for tracking analytics events.

Source code in src/zenml/models/v2/base/base.py

class BaseZenModel(YAMLSerializationMixin, AnalyticsTrackedModelMixin):
    """Base model class for all ZenML models.

    This class is used as a base class for all ZenML models. It provides
    functionality for tracking analytics events.
    """

    model_config = ConfigDict(
        # Ignore extras on all models to support forwards and backwards
        # compatibility (e.g. new fields in newer versions of ZenML servers
        # are allowed to be passed to older versions of ZenML clients and
        # vice versa but will be ignored).
        extra="ignore",
    )

`BoolFilter`

Bases: Filter

Filter for all Boolean fields.

Source code in src/zenml/models/v2/base/filter.py

class BoolFilter(Filter):
    """Filter for all Boolean fields."""

    ALLOWED_OPS: ClassVar[List[str]] = [
        GenericFilterOps.EQUALS,
        GenericFilterOps.NOT_EQUALS,
    ]

    def generate_query_conditions_from_column(self, column: Any) -> Any:
        """Generate query conditions for a boolean column.

        Args:
            column: The boolean column of an SQLModel table on which to filter.

        Returns:
            A list of query conditions.
        """
        if self.operation == GenericFilterOps.NOT_EQUALS:
            return column != self.value

        return column == self.value

`generate_query_conditions_from_column(column)`

Generate query conditions for a boolean column.

Parameters:

Name	Type	Description	Default
`column`	`Any`	The boolean column of an SQLModel table on which to filter.	required

Returns:

Type	Description
`Any`	A list of query conditions.

Source code in src/zenml/models/v2/base/filter.py

def generate_query_conditions_from_column(self, column: Any) -> Any:
    """Generate query conditions for a boolean column.

    Args:
        column: The boolean column of an SQLModel table on which to filter.

    Returns:
        A list of query conditions.
    """
    if self.operation == GenericFilterOps.NOT_EQUALS:
        return column != self.value

    return column == self.value

`BuildItem`

Bases: BaseModel

Pipeline build item.

Attributes:

Name	Type	Description
`image`	`str`	The image name or digest.
`dockerfile`	`Optional[str]`	The contents of the Dockerfile used to build the image.
`requirements`	`Optional[str]`	The pip requirements installed in the image. This is a string consisting of multiple concatenated requirements.txt files.
`settings_checksum`	`Optional[str]`	Checksum of the settings used for the build.
`contains_code`	`bool`	Whether the image contains user files.
`requires_code_download`	`bool`	Whether the image needs to download files.

Source code in src/zenml/models/v2/misc/build_item.py

class BuildItem(BaseModel):
    """Pipeline build item.

    Attributes:
        image: The image name or digest.
        dockerfile: The contents of the Dockerfile used to build the image.
        requirements: The pip requirements installed in the image. This is a
            string consisting of multiple concatenated requirements.txt files.
        settings_checksum: Checksum of the settings used for the build.
        contains_code: Whether the image contains user files.
        requires_code_download: Whether the image needs to download files.
    """

    image: str = Field(title="The image name or digest.")
    dockerfile: Optional[str] = Field(
        default=None, title="The dockerfile used to build the image."
    )
    requirements: Optional[str] = Field(
        default=None, title="The pip requirements installed in the image."
    )
    settings_checksum: Optional[str] = Field(
        default=None, title="The checksum of the build settings."
    )
    contains_code: bool = Field(
        default=True, title="Whether the image contains user files."
    )
    requires_code_download: bool = Field(
        default=False, title="Whether the image needs to download files."
    )

`CodeReferenceRequest`

Bases: BaseRequest

Request model for code references.

Source code in src/zenml/models/v2/core/code_reference.py

class CodeReferenceRequest(BaseRequest):
    """Request model for code references."""

    commit: str = Field(description="The commit of the code reference.")
    subdirectory: str = Field(
        description="The subdirectory of the code reference."
    )
    code_repository: UUID = Field(
        description="The repository of the code reference."
    )

`CodeReferenceResponse`

Bases: BaseIdentifiedResponse[CodeReferenceResponseBody, CodeReferenceResponseMetadata, CodeReferenceResponseResources]

Response model for code references.

Source code in src/zenml/models/v2/core/code_reference.py

class CodeReferenceResponse(
    BaseIdentifiedResponse[
        CodeReferenceResponseBody,
        CodeReferenceResponseMetadata,
        CodeReferenceResponseResources,
    ]
):
    """Response model for code references."""

    def get_hydrated_version(self) -> "CodeReferenceResponse":
        """Get the hydrated version of this code reference.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_code_reference(self.id)

    # Body and metadata properties
    @property
    def commit(self) -> str:
        """The `commit` property.

        Returns:
            the value of the property.
        """
        return self.get_body().commit

    @property
    def subdirectory(self) -> str:
        """The `subdirectory` property.

        Returns:
            the value of the property.
        """
        return self.get_body().subdirectory

    @property
    def code_repository(self) -> "CodeRepositoryResponse":
        """The `code_repository` property.

        Returns:
            the value of the property.
        """
        return self.get_body().code_repository

`code_repository` `property`

The code_repository property.

Returns:

Type	Description
`CodeRepositoryResponse`	the value of the property.

`commit` `property`

The commit property.

Returns:

Type	Description
`str`	the value of the property.

`subdirectory` `property`

The subdirectory property.

Returns:

Type	Description
`str`	the value of the property.

`get_hydrated_version()`

Get the hydrated version of this code reference.

Returns:

Type	Description
`CodeReferenceResponse`	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/code_reference.py

def get_hydrated_version(self) -> "CodeReferenceResponse":
    """Get the hydrated version of this code reference.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_code_reference(self.id)

`CodeReferenceResponseBody`

Response body for code references.

Source code in src/zenml/models/v2/core/code_reference.py

class CodeReferenceResponseBody(BaseDatedResponseBody):
    """Response body for code references."""

    commit: str = Field(description="The commit of the code reference.")
    subdirectory: str = Field(
        description="The subdirectory of the code reference."
    )
    code_repository: "CodeRepositoryResponse" = Field(
        description="The repository of the code reference."
    )

`CodeReferenceResponseMetadata`

Bases: BaseResponseMetadata

Response metadata for code references.

Source code in src/zenml/models/v2/core/code_reference.py

class CodeReferenceResponseMetadata(BaseResponseMetadata):
    """Response metadata for code references."""

`CodeRepositoryFilter`

Model to enable advanced filtering of all code repositories.

Source code in src/zenml/models/v2/core/code_repository.py

class CodeRepositoryFilter(WorkspaceScopedFilter):
    """Model to enable advanced filtering of all code repositories."""

    name: Optional[str] = Field(
        description="Name of the code repository.",
        default=None,
    )

`CodeRepositoryRequest`

Request model for code repositories.

Source code in src/zenml/models/v2/core/code_repository.py

class CodeRepositoryRequest(WorkspaceScopedRequest):
    """Request model for code repositories."""

    name: str = Field(
        title="The name of the code repository.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    config: Dict[str, Any] = Field(
        description="Configuration for the code repository."
    )
    source: Source = Field(description="The code repository source.")
    logo_url: Optional[str] = Field(
        description="Optional URL of a logo (png, jpg or svg) for the "
        "code repository.",
        default=None,
    )
    description: Optional[str] = Field(
        description="Code repository description.",
        max_length=TEXT_FIELD_MAX_LENGTH,
        default=None,
    )

`CodeRepositoryResponse`

Bases: WorkspaceScopedResponse[CodeRepositoryResponseBody, CodeRepositoryResponseMetadata, CodeRepositoryResponseResources]

Response model for code repositories.

Source code in src/zenml/models/v2/core/code_repository.py

class CodeRepositoryResponse(
    WorkspaceScopedResponse[
        CodeRepositoryResponseBody,
        CodeRepositoryResponseMetadata,
        CodeRepositoryResponseResources,
    ]
):
    """Response model for code repositories."""

    name: str = Field(
        title="The name of the code repository.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    def get_hydrated_version(self) -> "CodeRepositoryResponse":
        """Get the hydrated version of this code repository.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_code_repository(self.id)

    # Body and metadata properties
    @property
    def source(self) -> Source:
        """The `source` property.

        Returns:
            the value of the property.
        """
        return self.get_body().source

    @property
    def logo_url(self) -> Optional[str]:
        """The `logo_url` property.

        Returns:
            the value of the property.
        """
        return self.get_body().logo_url

    @property
    def config(self) -> Dict[str, Any]:
        """The `config` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().config

    @property
    def description(self) -> Optional[str]:
        """The `description` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().description

`config` `property`

The config property.

Returns:

Type	Description
`Dict[str, Any]`	the value of the property.

`description` `property`

The description property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`logo_url` `property`

The logo_url property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`source` `property`

The source property.

Returns:

Type	Description
`Source`	the value of the property.

`get_hydrated_version()`

Get the hydrated version of this code repository.

Returns:

Type	Description
`CodeRepositoryResponse`	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/code_repository.py

def get_hydrated_version(self) -> "CodeRepositoryResponse":
    """Get the hydrated version of this code repository.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_code_repository(self.id)

`CodeRepositoryResponseBody`

Response body for code repositories.

Source code in src/zenml/models/v2/core/code_repository.py

class CodeRepositoryResponseBody(WorkspaceScopedResponseBody):
    """Response body for code repositories."""

    source: Source = Field(description="The code repository source.")
    logo_url: Optional[str] = Field(
        default=None,
        description="Optional URL of a logo (png, jpg or svg) for the "
        "code repository.",
    )

`CodeRepositoryResponseMetadata`

Response metadata for code repositories.

Source code in src/zenml/models/v2/core/code_repository.py

class CodeRepositoryResponseMetadata(WorkspaceScopedResponseMetadata):
    """Response metadata for code repositories."""

    config: Dict[str, Any] = Field(
        description="Configuration for the code repository."
    )
    description: Optional[str] = Field(
        default=None,
        description="Code repository description.",
        max_length=TEXT_FIELD_MAX_LENGTH,
    )

`CodeRepositoryUpdate`

Bases: BaseUpdate

Update model for code repositories.

Source code in src/zenml/models/v2/core/code_repository.py

class CodeRepositoryUpdate(BaseUpdate):
    """Update model for code repositories."""

    name: Optional[str] = Field(
        title="The name of the code repository.",
        max_length=STR_FIELD_MAX_LENGTH,
        default=None,
    )
    config: Optional[Dict[str, Any]] = Field(
        description="Configuration for the code repository.",
        default=None,
    )
    source: Optional[SourceWithValidator] = Field(
        description="The code repository source.", default=None
    )
    logo_url: Optional[str] = Field(
        description="Optional URL of a logo (png, jpg or svg) for the "
        "code repository.",
        default=None,
    )
    description: Optional[str] = Field(
        description="Code repository description.",
        max_length=TEXT_FIELD_MAX_LENGTH,
        default=None,
    )

`ComponentBase`

Bases: BaseModel

Base model for components.

Source code in src/zenml/models/v2/core/component.py

class ComponentBase(BaseModel):
    """Base model for components."""

    name: str = Field(
        title="The name of the stack component.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    type: StackComponentType = Field(
        title="The type of the stack component.",
    )

    flavor: str = Field(
        title="The flavor of the stack component.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    configuration: Dict[str, Any] = Field(
        title="The stack component configuration.",
    )

    connector_resource_id: Optional[str] = Field(
        default=None,
        description="The ID of a specific resource instance to "
        "gain access to through the connector",
    )

    labels: Optional[Dict[str, Any]] = Field(
        default=None,
        title="The stack component labels.",
    )

`ComponentFilter`

Model to enable advanced filtering of all ComponentModels.

The Component Model needs additional scoping. As such the _scope_user field can be set to the user that is doing the filtering. The generate_filter() method of the baseclass is overwritten to include the scoping.

Source code in src/zenml/models/v2/core/component.py

class ComponentFilter(WorkspaceScopedFilter):
    """Model to enable advanced filtering of all ComponentModels.

    The Component Model needs additional scoping. As such the `_scope_user`
    field can be set to the user that is doing the filtering. The
    `generate_filter()` method of the baseclass is overwritten to include the
    scoping.
    """

    FILTER_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        *WorkspaceScopedFilter.FILTER_EXCLUDE_FIELDS,
        "scope_type",
        "stack_id",
    ]
    CLI_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        *WorkspaceScopedFilter.CLI_EXCLUDE_FIELDS,
        "scope_type",
    ]
    scope_type: Optional[str] = Field(
        default=None,
        description="The type to scope this query to.",
    )
    name: Optional[str] = Field(
        default=None,
        description="Name of the stack component",
    )
    flavor: Optional[str] = Field(
        default=None,
        description="Flavor of the stack component",
    )
    type: Optional[str] = Field(
        default=None,
        description="Type of the stack component",
    )
    connector_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Connector linked to the stack component",
        union_mode="left_to_right",
    )
    stack_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Stack of the stack component",
        union_mode="left_to_right",
    )

    def set_scope_type(self, component_type: str) -> None:
        """Set the type of component on which to perform the filtering to scope the response.

        Args:
            component_type: The type of component to scope the query to.
        """
        self.scope_type = component_type

    def generate_filter(
        self, table: Type["AnySchema"]
    ) -> Union["ColumnElement[bool]"]:
        """Generate the filter for the query.

        Stack components can be scoped by type to narrow the search.

        Args:
            table: The Table that is being queried from.

        Returns:
            The filter expression for the query.
        """
        from sqlmodel import and_, or_

        from zenml.zen_stores.schemas import (
            StackComponentSchema,
            StackCompositionSchema,
        )

        base_filter = super().generate_filter(table)
        if self.scope_type:
            type_filter = getattr(table, "type") == self.scope_type
            return and_(base_filter, type_filter)

        if self.stack_id:
            operator = (
                or_ if self.logical_operator == LogicalOperators.OR else and_
            )

            stack_filter = and_(
                StackCompositionSchema.stack_id == self.stack_id,
                StackCompositionSchema.component_id == StackComponentSchema.id,
            )
            base_filter = operator(base_filter, stack_filter)

        return base_filter

`generate_filter(table)`

Generate the filter for the query.

Stack components can be scoped by type to narrow the search.

Parameters:

Name	Type	Description	Default
`table`	`Type[AnySchema]`	The Table that is being queried from.	required

Returns:

Type	Description
`Union[ColumnElement[bool]]`	The filter expression for the query.

Source code in src/zenml/models/v2/core/component.py

def generate_filter(
    self, table: Type["AnySchema"]
) -> Union["ColumnElement[bool]"]:
    """Generate the filter for the query.

    Stack components can be scoped by type to narrow the search.

    Args:
        table: The Table that is being queried from.

    Returns:
        The filter expression for the query.
    """
    from sqlmodel import and_, or_

    from zenml.zen_stores.schemas import (
        StackComponentSchema,
        StackCompositionSchema,
    )

    base_filter = super().generate_filter(table)
    if self.scope_type:
        type_filter = getattr(table, "type") == self.scope_type
        return and_(base_filter, type_filter)

    if self.stack_id:
        operator = (
            or_ if self.logical_operator == LogicalOperators.OR else and_
        )

        stack_filter = and_(
            StackCompositionSchema.stack_id == self.stack_id,
            StackCompositionSchema.component_id == StackComponentSchema.id,
        )
        base_filter = operator(base_filter, stack_filter)

    return base_filter

`set_scope_type(component_type)`

Set the type of component on which to perform the filtering to scope the response.

Parameters:

Name	Type	Description	Default
`component_type`	`str`	The type of component to scope the query to.	required

Source code in src/zenml/models/v2/core/component.py

def set_scope_type(self, component_type: str) -> None:
    """Set the type of component on which to perform the filtering to scope the response.

    Args:
        component_type: The type of component to scope the query to.
    """
    self.scope_type = component_type

`ComponentInfo`

Bases: BaseModel

Information about each stack components when creating a full stack.

Source code in src/zenml/models/v2/misc/info_models.py

class ComponentInfo(BaseModel):
    """Information about each stack components when creating a full stack."""

    flavor: str
    service_connector_index: Optional[int] = Field(
        default=None,
        title="The id of the service connector from the list "
        "`service_connectors`.",
        description="The id of the service connector from the list "
        "`service_connectors` from `FullStackRequest`.",
    )
    service_connector_resource_id: Optional[str] = None
    configuration: Dict[str, Any] = {}

`ComponentRequest`

Bases: ComponentBase, WorkspaceScopedRequest

Request model for components.

Source code in src/zenml/models/v2/core/component.py

class ComponentRequest(ComponentBase, WorkspaceScopedRequest):
    """Request model for components."""

    ANALYTICS_FIELDS: ClassVar[List[str]] = ["type", "flavor"]

    connector: Optional[UUID] = Field(
        default=None,
        title="The service connector linked to this stack component.",
    )

    @field_validator("name")
    @classmethod
    def name_cant_be_a_secret_reference(cls, name: str) -> str:
        """Validator to ensure that the given name is not a secret reference.

        Args:
            name: The name to validate.

        Returns:
            The name if it is not a secret reference.

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

`name_cant_be_a_secret_reference(name)` `classmethod`

Validator to ensure that the given name is not a secret reference.

Parameters:

Name	Type	Description	Default
`name`	`str`	The name to validate.	required

Returns:

Type	Description
`str`	The name if it is not a secret reference.

Raises:

Type	Description
`ValueError`	If the name is a secret reference.

Source code in src/zenml/models/v2/core/component.py

@field_validator("name")
@classmethod
def name_cant_be_a_secret_reference(cls, name: str) -> str:
    """Validator to ensure that the given name is not a secret reference.

    Args:
        name: The name to validate.

    Returns:
        The name if it is not a secret reference.

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

`ComponentResponse`

Bases: WorkspaceScopedResponse[ComponentResponseBody, ComponentResponseMetadata, ComponentResponseResources]

Response model for components.

Source code in src/zenml/models/v2/core/component.py

class ComponentResponse(
    WorkspaceScopedResponse[
        ComponentResponseBody,
        ComponentResponseMetadata,
        ComponentResponseResources,
    ]
):
    """Response model for components."""

    ANALYTICS_FIELDS: ClassVar[List[str]] = ["type"]

    name: str = Field(
        title="The name of the stack component.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    def get_analytics_metadata(self) -> Dict[str, Any]:
        """Add the component labels to analytics metadata.

        Returns:
            Dict of analytics metadata.
        """
        metadata = super().get_analytics_metadata()

        if self.labels is not None:
            metadata.update(
                {
                    label[6:]: value
                    for label, value in self.labels.items()
                    if label.startswith("zenml:")
                }
            )
        metadata["flavor"] = self.flavor_name

        return metadata

    def get_hydrated_version(self) -> "ComponentResponse":
        """Get the hydrated version of this component.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_stack_component(self.id)

    # Body and metadata properties
    @property
    def type(self) -> StackComponentType:
        """The `type` property.

        Returns:
            the value of the property.
        """
        return self.get_body().type

    @property
    def flavor_name(self) -> str:
        """The `flavor_name` property.

        Returns:
            the value of the property.
        """
        return self.get_body().flavor_name

    @property
    def integration(self) -> Optional[str]:
        """The `integration` property.

        Returns:
            the value of the property.
        """
        return self.get_body().integration

    @property
    def logo_url(self) -> Optional[str]:
        """The `logo_url` property.

        Returns:
            the value of the property.
        """
        return self.get_body().logo_url

    @property
    def configuration(self) -> Dict[str, Any]:
        """The `configuration` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().configuration

    @property
    def labels(self) -> Optional[Dict[str, Any]]:
        """The `labels` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().labels

    @property
    def connector_resource_id(self) -> Optional[str]:
        """The `connector_resource_id` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().connector_resource_id

    @property
    def connector(self) -> Optional["ServiceConnectorResponse"]:
        """The `connector` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().connector

    @property
    def flavor(self) -> "FlavorResponse":
        """The `flavor` property.

        Returns:
            the value of the property.
        """
        return self.get_resources().flavor

`configuration` `property`

The configuration property.

Returns:

Type	Description
`Dict[str, Any]`	the value of the property.

`connector` `property`

The connector property.

Returns:

Type	Description
`Optional[ServiceConnectorResponse]`	the value of the property.

`connector_resource_id` `property`

The connector_resource_id property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`flavor` `property`

The flavor property.

Returns:

Type	Description
`FlavorResponse`	the value of the property.

`flavor_name` `property`

The flavor_name property.

Returns:

Type	Description
`str`	the value of the property.

`integration` `property`

The integration property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`labels` `property`

The labels property.

Returns:

Type	Description
`Optional[Dict[str, Any]]`	the value of the property.

`logo_url` `property`

The logo_url property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`type` `property`

The type property.

Returns:

Type	Description
`StackComponentType`	the value of the property.

`get_analytics_metadata()`

Add the component labels to analytics metadata.

Returns:

Type	Description
`Dict[str, Any]`	Dict of analytics metadata.

Source code in src/zenml/models/v2/core/component.py

def get_analytics_metadata(self) -> Dict[str, Any]:
    """Add the component labels to analytics metadata.

    Returns:
        Dict of analytics metadata.
    """
    metadata = super().get_analytics_metadata()

    if self.labels is not None:
        metadata.update(
            {
                label[6:]: value
                for label, value in self.labels.items()
                if label.startswith("zenml:")
            }
        )
    metadata["flavor"] = self.flavor_name

    return metadata

`get_hydrated_version()`

Get the hydrated version of this component.

Returns:

Type	Description
`ComponentResponse`	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/component.py

def get_hydrated_version(self) -> "ComponentResponse":
    """Get the hydrated version of this component.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_stack_component(self.id)

`ComponentResponseBody`

Response body for components.

Source code in src/zenml/models/v2/core/component.py

class ComponentResponseBody(WorkspaceScopedResponseBody):
    """Response body for components."""

    type: StackComponentType = Field(
        title="The type of the stack component.",
    )
    flavor_name: str = Field(
        title="The flavor of the stack component.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    integration: Optional[str] = Field(
        default=None,
        title="The name of the integration that the component's flavor "
        "belongs to.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    logo_url: Optional[str] = Field(
        default=None,
        title="Optionally, a url pointing to a png,"
        "svg or jpg can be attached.",
    )

`ComponentResponseMetadata`

Response metadata for components.

Source code in src/zenml/models/v2/core/component.py

class ComponentResponseMetadata(WorkspaceScopedResponseMetadata):
    """Response metadata for components."""

    configuration: Dict[str, Any] = Field(
        title="The stack component configuration.",
    )
    labels: Optional[Dict[str, Any]] = Field(
        default=None,
        title="The stack component labels.",
    )
    connector_resource_id: Optional[str] = Field(
        default=None,
        description="The ID of a specific resource instance to "
        "gain access to through the connector",
    )
    connector: Optional["ServiceConnectorResponse"] = Field(
        default=None,
        title="The service connector linked to this stack component.",
    )

`ComponentResponseResources`

Class for all resource models associated with the component entity.

Source code in src/zenml/models/v2/core/component.py

class ComponentResponseResources(WorkspaceScopedResponseResources):
    """Class for all resource models associated with the component entity."""

    flavor: "FlavorResponse" = Field(
        title="The flavor of this stack component.",
    )

`ComponentUpdate`

Bases: BaseUpdate

Update model for stack components.

Source code in src/zenml/models/v2/core/component.py

class ComponentUpdate(BaseUpdate):
    """Update model for stack components."""

    name: Optional[str] = Field(
        title="The name of the stack component.",
        max_length=STR_FIELD_MAX_LENGTH,
        default=None,
    )
    configuration: Optional[Dict[str, Any]] = Field(
        title="The stack component configuration.",
        default=None,
    )
    connector_resource_id: Optional[str] = Field(
        description="The ID of a specific resource instance to "
        "gain access to through the connector",
        default=None,
    )
    labels: Optional[Dict[str, Any]] = Field(
        title="The stack component labels.",
        default=None,
    )
    connector: Optional[UUID] = Field(
        title="The service connector linked to this stack component.",
        default=None,
    )

`DeployedStack`

Bases: BaseModel

Information about a deployed stack.

Source code in src/zenml/models/v2/misc/stack_deployment.py

class DeployedStack(BaseModel):
    """Information about a deployed stack."""

    stack: StackResponse = Field(
        title="The stack that was deployed.",
        description="The stack that was deployed.",
    )
    service_connector: Optional[ServiceConnectorResponse] = Field(
        default=None,
        title="The service connector for the deployed stack.",
        description="The service connector for the deployed stack.",
    )

`EventSourceFilter`

Model to enable advanced filtering of all EventSourceModels.

Source code in src/zenml/models/v2/core/event_source.py

class EventSourceFilter(WorkspaceScopedFilter):
    """Model to enable advanced filtering of all EventSourceModels."""

    name: Optional[str] = Field(
        default=None,
        description="Name of the event source",
    )
    flavor: Optional[str] = Field(
        default=None,
        description="Flavor of the event source",
    )
    plugin_subtype: Optional[str] = Field(
        default=None,
        title="The plugin sub type of the event source.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

`EventSourceFlavorResponse`

Bases: BasePluginFlavorResponse[EventSourceFlavorResponseBody, EventSourceFlavorResponseMetadata, EventSourceFlavorResponseResources]

Response model for Event Source Flavors.

Source code in src/zenml/models/v2/core/event_source_flavor.py

class EventSourceFlavorResponse(
    BasePluginFlavorResponse[
        EventSourceFlavorResponseBody,
        EventSourceFlavorResponseMetadata,
        EventSourceFlavorResponseResources,
    ]
):
    """Response model for Event Source Flavors."""

    # Body and metadata properties
    @property
    def source_config_schema(self) -> Dict[str, Any]:
        """The `source_config_schema` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().source_config_schema

    @property
    def filter_config_schema(self) -> Dict[str, Any]:
        """The `filter_config_schema` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().filter_config_schema

`filter_config_schema` `property`

The filter_config_schema property.

Returns:

Type	Description
`Dict[str, Any]`	the value of the property.

`source_config_schema` `property`

The source_config_schema property.

Returns:

Type	Description
`Dict[str, Any]`	the value of the property.

`EventSourceFlavorResponseBody`

Bases: BasePluginResponseBody

Response body for event flavors.

Source code in src/zenml/models/v2/core/event_source_flavor.py

class EventSourceFlavorResponseBody(BasePluginResponseBody):
    """Response body for event flavors."""

`EventSourceFlavorResponseMetadata`

Bases: BasePluginResponseMetadata

Response metadata for event flavors.

Source code in src/zenml/models/v2/core/event_source_flavor.py

class EventSourceFlavorResponseMetadata(BasePluginResponseMetadata):
    """Response metadata for event flavors."""

    source_config_schema: Dict[str, Any]
    filter_config_schema: Dict[str, Any]

`EventSourceFlavorResponseResources`

Bases: BasePluginResponseResources

Response resources for event source flavors.

Source code in src/zenml/models/v2/core/event_source_flavor.py

class EventSourceFlavorResponseResources(BasePluginResponseResources):
    """Response resources for event source flavors."""

`EventSourceRequest`

BaseModel for all event sources.

Source code in src/zenml/models/v2/core/event_source.py

class EventSourceRequest(WorkspaceScopedRequest):
    """BaseModel for all event sources."""

    name: str = Field(
        title="The name of the event source.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    flavor: str = Field(
        title="The flavor of event source.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    plugin_subtype: PluginSubType = Field(
        title="The plugin subtype of the event source.",
    )
    description: str = Field(
        default="",
        title="The description of the event source.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    configuration: Dict[str, Any] = Field(
        title="The event source configuration.",
    )

`EventSourceResponse`

Bases: WorkspaceScopedResponse[EventSourceResponseBody, EventSourceResponseMetadata, EventSourceResponseResources]

Response model for event sources.

Source code in src/zenml/models/v2/core/event_source.py

class EventSourceResponse(
    WorkspaceScopedResponse[
        EventSourceResponseBody,
        EventSourceResponseMetadata,
        EventSourceResponseResources,
    ]
):
    """Response model for event sources."""

    name: str = Field(
        title="The name of the event source.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    def get_hydrated_version(self) -> "EventSourceResponse":
        """Get the hydrated version of this event source.

        Returns:
            An instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_event_source(self.id)

    # Body and metadata properties
    @property
    def flavor(self) -> str:
        """The `flavor` property.

        Returns:
            the value of the property.
        """
        return self.get_body().flavor

    @property
    def is_active(self) -> bool:
        """The `is_active` property.

        Returns:
            the value of the property.
        """
        return self.get_body().is_active

    @property
    def plugin_subtype(self) -> PluginSubType:
        """The `plugin_subtype` property.

        Returns:
            the value of the property.
        """
        return self.get_body().plugin_subtype

    @property
    def description(self) -> str:
        """The `description` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().description

    @property
    def configuration(self) -> Dict[str, Any]:
        """The `configuration` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().configuration

    def set_configuration(self, configuration: Dict[str, Any]) -> None:
        """Set the `configuration` property.

        Args:
            configuration: The value to set.
        """
        self.get_metadata().configuration = configuration

`configuration` `property`

The configuration property.

Returns:

Type	Description
`Dict[str, Any]`	the value of the property.

`description` `property`

The description property.

Returns:

Type	Description
`str`	the value of the property.

`flavor` `property`

The flavor property.

Returns:

Type	Description
`str`	the value of the property.

`is_active` `property`

The is_active property.

Returns:

Type	Description
`bool`	the value of the property.

`plugin_subtype` `property`

The plugin_subtype property.

Returns:

Type	Description
`PluginSubType`	the value of the property.

`get_hydrated_version()`

Get the hydrated version of this event source.

Returns:

Type	Description
`EventSourceResponse`	An instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/event_source.py

def get_hydrated_version(self) -> "EventSourceResponse":
    """Get the hydrated version of this event source.

    Returns:
        An instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_event_source(self.id)

`set_configuration(configuration)`

Set the configuration property.

Parameters:

Name	Type	Description	Default
`configuration`	`Dict[str, Any]`	The value to set.	required

Source code in src/zenml/models/v2/core/event_source.py

def set_configuration(self, configuration: Dict[str, Any]) -> None:
    """Set the `configuration` property.

    Args:
        configuration: The value to set.
    """
    self.get_metadata().configuration = configuration

`EventSourceResponseBody`

ResponseBody for event sources.

Source code in src/zenml/models/v2/core/event_source.py

class EventSourceResponseBody(WorkspaceScopedResponseBody):
    """ResponseBody for event sources."""

    flavor: str = Field(
        title="The flavor of event source.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    plugin_subtype: PluginSubType = Field(
        title="The plugin subtype of the event source.",
    )
    is_active: bool = Field(
        title="Whether the event source is active.",
    )

`EventSourceResponseMetadata`

Response metadata for event sources.

Source code in src/zenml/models/v2/core/event_source.py

class EventSourceResponseMetadata(WorkspaceScopedResponseMetadata):
    """Response metadata for event sources."""

    description: str = Field(
        default="",
        title="The description of the event source.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    configuration: Dict[str, Any] = Field(
        title="The event source configuration.",
    )

`EventSourceResponseResources`

Class for all resource models associated with the code repository entity.

Source code in src/zenml/models/v2/core/event_source.py

class EventSourceResponseResources(WorkspaceScopedResponseResources):
    """Class for all resource models associated with the code repository entity."""

    triggers: Page[TriggerResponse] = Field(
        title="The triggers configured with this event source.",
    )

`EventSourceUpdate`

Bases: BaseZenModel

Update model for event sources.

Source code in src/zenml/models/v2/core/event_source.py

class EventSourceUpdate(BaseZenModel):
    """Update model for event sources."""

    name: Optional[str] = Field(
        default=None,
        title="The updated name of the event source.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    description: Optional[str] = Field(
        default=None,
        title="The updated description of the event source.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    configuration: Optional[Dict[str, Any]] = Field(
        default=None,
        title="The updated event source configuration.",
    )
    is_active: Optional[bool] = Field(
        default=None,
        title="The status of the event source.",
    )

    @classmethod
    def from_response(
        cls, response: "EventSourceResponse"
    ) -> "EventSourceUpdate":
        """Create an update model from a response model.

        Args:
            response: The response model to create the update model from.

        Returns:
            The update model.
        """
        return EventSourceUpdate(
            name=response.name,
            description=response.description,
            configuration=copy.deepcopy(response.configuration),
            is_active=response.is_active,
        )

`from_response(response)` `classmethod`

Create an update model from a response model.

Parameters:

Name	Type	Description	Default
`response`	`EventSourceResponse`	The response model to create the update model from.	required

Returns:

Type	Description
`EventSourceUpdate`	The update model.

Source code in src/zenml/models/v2/core/event_source.py

@classmethod
def from_response(
    cls, response: "EventSourceResponse"
) -> "EventSourceUpdate":
    """Create an update model from a response model.

    Args:
        response: The response model to create the update model from.

    Returns:
        The update model.
    """
    return EventSourceUpdate(
        name=response.name,
        description=response.description,
        configuration=copy.deepcopy(response.configuration),
        is_active=response.is_active,
    )

`ExternalUserModel`

Bases: BaseModel

External user model.

Source code in src/zenml/models/v2/misc/external_user.py

class ExternalUserModel(BaseModel):
    """External user model."""

    id: UUID
    email: str
    name: Optional[str] = None
    is_admin: bool = False

    model_config = ConfigDict(extra="ignore")

`FlavorFilter`

Model to enable advanced filtering of all Flavors.

Source code in src/zenml/models/v2/core/flavor.py

class FlavorFilter(WorkspaceScopedFilter):
    """Model to enable advanced filtering of all Flavors."""

    name: Optional[str] = Field(
        default=None,
        description="Name of the flavor",
    )
    type: Optional[str] = Field(
        default=None,
        description="Stack Component Type of the stack flavor",
    )
    integration: Optional[str] = Field(
        default=None,
        description="Integration associated with the flavor",
    )

`FlavorRequest`

Bases: UserScopedRequest

Request model for flavors.

Source code in src/zenml/models/v2/core/flavor.py

class FlavorRequest(UserScopedRequest):
    """Request model for flavors."""

    ANALYTICS_FIELDS: ClassVar[List[str]] = [
        "type",
        "integration",
    ]

    name: str = Field(
        title="The name of the Flavor.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    type: StackComponentType = Field(title="The type of the Flavor.")
    config_schema: Dict[str, Any] = Field(
        title="The JSON schema of this flavor's corresponding configuration.",
    )
    connector_type: Optional[str] = Field(
        default=None,
        title="The type of the connector that this flavor uses.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    connector_resource_type: Optional[str] = Field(
        default=None,
        title="The resource type of the connector that this flavor uses.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    connector_resource_id_attr: Optional[str] = Field(
        default=None,
        title="The name of an attribute in the stack component configuration "
        "that plays the role of resource ID when linked to a service "
        "connector.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    source: str = Field(
        title="The path to the module which contains this Flavor.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    integration: Optional[str] = Field(
        title="The name of the integration that the Flavor belongs to.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    logo_url: Optional[str] = Field(
        default=None,
        title="Optionally, a url pointing to a png,"
        "svg or jpg can be attached.",
    )
    docs_url: Optional[str] = Field(
        default=None,
        title="Optionally, a url pointing to docs, within docs.zenml.io.",
    )
    sdk_docs_url: Optional[str] = Field(
        default=None,
        title="Optionally, a url pointing to SDK docs,"
        "within sdkdocs.zenml.io.",
    )
    is_custom: bool = Field(
        title="Whether or not this flavor is a custom, user created flavor.",
        default=True,
    )
    workspace: Optional[UUID] = Field(
        default=None, title="The workspace to which this resource belongs."
    )

`FlavorResponse`

Bases: UserScopedResponse[FlavorResponseBody, FlavorResponseMetadata, FlavorResponseResources]

Response model for flavors.

Source code in src/zenml/models/v2/core/flavor.py

class FlavorResponse(
    UserScopedResponse[
        FlavorResponseBody, FlavorResponseMetadata, FlavorResponseResources
    ]
):
    """Response model for flavors."""

    # Analytics
    ANALYTICS_FIELDS: ClassVar[List[str]] = [
        "id",
        "type",
        "integration",
    ]

    name: str = Field(
        title="The name of the Flavor.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    def get_hydrated_version(self) -> "FlavorResponse":
        """Get the hydrated version of the flavor.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_flavor(self.id)

    # Helper methods
    @property
    def connector_requirements(
        self,
    ) -> Optional["ServiceConnectorRequirements"]:
        """Returns the connector requirements for the flavor.

        Returns:
            The connector requirements for the flavor.
        """
        from zenml.models import (
            ServiceConnectorRequirements,
        )

        if not self.connector_resource_type:
            return None

        return ServiceConnectorRequirements(
            connector_type=self.connector_type,
            resource_type=self.connector_resource_type,
            resource_id_attr=self.connector_resource_id_attr,
        )

    # Body and metadata properties
    @property
    def type(self) -> StackComponentType:
        """The `type` property.

        Returns:
            the value of the property.
        """
        return self.get_body().type

    @property
    def integration(self) -> Optional[str]:
        """The `integration` property.

        Returns:
            the value of the property.
        """
        return self.get_body().integration

    @property
    def source(self) -> str:
        """The `source` property.

        Returns:
            the value of the property.
        """
        return self.get_body().source

    @property
    def logo_url(self) -> Optional[str]:
        """The `logo_url` property.

        Returns:
            the value of the property.
        """
        return self.get_body().logo_url

    @property
    def workspace(self) -> Optional["WorkspaceResponse"]:
        """The `workspace` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().workspace

    @property
    def config_schema(self) -> Dict[str, Any]:
        """The `config_schema` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().config_schema

    @property
    def connector_type(self) -> Optional[str]:
        """The `connector_type` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().connector_type

    @property
    def connector_resource_type(self) -> Optional[str]:
        """The `connector_resource_type` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().connector_resource_type

    @property
    def connector_resource_id_attr(self) -> Optional[str]:
        """The `connector_resource_id_attr` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().connector_resource_id_attr

    @property
    def docs_url(self) -> Optional[str]:
        """The `docs_url` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().docs_url

    @property
    def sdk_docs_url(self) -> Optional[str]:
        """The `sdk_docs_url` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().sdk_docs_url

    @property
    def is_custom(self) -> bool:
        """The `is_custom` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().is_custom

`config_schema` `property`

The config_schema property.

Returns:

Type	Description
`Dict[str, Any]`	the value of the property.

`connector_requirements` `property`

Returns the connector requirements for the flavor.

Returns:

Type	Description
`Optional[ServiceConnectorRequirements]`	The connector requirements for the flavor.

`connector_resource_id_attr` `property`

The connector_resource_id_attr property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`connector_resource_type` `property`

The connector_resource_type property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`connector_type` `property`

The connector_type property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`docs_url` `property`

The docs_url property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`integration` `property`

The integration property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`is_custom` `property`

The is_custom property.

Returns:

Type	Description
`bool`	the value of the property.

`logo_url` `property`

The logo_url property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`sdk_docs_url` `property`

The sdk_docs_url property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`source` `property`

The source property.

Returns:

Type	Description
`str`	the value of the property.

`type` `property`

The type property.

Returns:

Type	Description
`StackComponentType`	the value of the property.

`workspace` `property`

The workspace property.

Returns:

Type	Description
`Optional[WorkspaceResponse]`	the value of the property.

`get_hydrated_version()`

Get the hydrated version of the flavor.

Returns:

Type	Description
`FlavorResponse`	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/flavor.py

def get_hydrated_version(self) -> "FlavorResponse":
    """Get the hydrated version of the flavor.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_flavor(self.id)

`FlavorResponseBody`

Bases: UserScopedResponseBody

Response body for flavor.

Source code in src/zenml/models/v2/core/flavor.py

class FlavorResponseBody(UserScopedResponseBody):
    """Response body for flavor."""

    type: StackComponentType = Field(title="The type of the Flavor.")
    integration: Optional[str] = Field(
        title="The name of the integration that the Flavor belongs to.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    source: str = Field(
        title="The path to the module which contains this Flavor.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    logo_url: Optional[str] = Field(
        default=None,
        title="Optionally, a url pointing to a png,"
        "svg or jpg can be attached.",
    )

`FlavorResponseMetadata`

Bases: UserScopedResponseMetadata

Response metadata for flavors.

Source code in src/zenml/models/v2/core/flavor.py

class FlavorResponseMetadata(UserScopedResponseMetadata):
    """Response metadata for flavors."""

    workspace: Optional["WorkspaceResponse"] = Field(
        title="The project of this resource."
    )
    config_schema: Dict[str, Any] = Field(
        title="The JSON schema of this flavor's corresponding configuration.",
    )
    connector_type: Optional[str] = Field(
        default=None,
        title="The type of the connector that this flavor uses.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    connector_resource_type: Optional[str] = Field(
        default=None,
        title="The resource type of the connector that this flavor uses.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    connector_resource_id_attr: Optional[str] = Field(
        default=None,
        title="The name of an attribute in the stack component configuration "
        "that plays the role of resource ID when linked to a service "
        "connector.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    docs_url: Optional[str] = Field(
        default=None,
        title="Optionally, a url pointing to docs, within docs.zenml.io.",
    )
    sdk_docs_url: Optional[str] = Field(
        default=None,
        title="Optionally, a url pointing to SDK docs,"
        "within sdkdocs.zenml.io.",
    )
    is_custom: bool = Field(
        title="Whether or not this flavor is a custom, user created flavor.",
        default=True,
    )

`FlavorUpdate`

Bases: BaseUpdate

Update model for flavors.

Source code in src/zenml/models/v2/core/flavor.py

class FlavorUpdate(BaseUpdate):
    """Update model for flavors."""

    name: Optional[str] = Field(
        title="The name of the Flavor.",
        max_length=STR_FIELD_MAX_LENGTH,
        default=None,
    )
    type: Optional[StackComponentType] = Field(
        title="The type of the Flavor.", default=None
    )
    config_schema: Optional[Dict[str, Any]] = Field(
        title="The JSON schema of this flavor's corresponding configuration.",
        default=None,
    )
    connector_type: Optional[str] = Field(
        title="The type of the connector that this flavor uses.",
        max_length=STR_FIELD_MAX_LENGTH,
        default=None,
    )
    connector_resource_type: Optional[str] = Field(
        title="The resource type of the connector that this flavor uses.",
        max_length=STR_FIELD_MAX_LENGTH,
        default=None,
    )
    connector_resource_id_attr: Optional[str] = Field(
        title="The name of an attribute in the stack component configuration "
        "that plays the role of resource ID when linked to a service "
        "connector.",
        max_length=STR_FIELD_MAX_LENGTH,
        default=None,
    )
    source: Optional[str] = Field(
        title="The path to the module which contains this Flavor.",
        max_length=STR_FIELD_MAX_LENGTH,
        default=None,
    )
    integration: Optional[str] = Field(
        title="The name of the integration that the Flavor belongs to.",
        max_length=STR_FIELD_MAX_LENGTH,
        default=None,
    )
    logo_url: Optional[str] = Field(
        title="Optionally, a url pointing to a png,"
        "svg or jpg can be attached.",
        default=None,
    )
    docs_url: Optional[str] = Field(
        title="Optionally, a url pointing to docs, within docs.zenml.io.",
        default=None,
    )
    sdk_docs_url: Optional[str] = Field(
        title="Optionally, a url pointing to SDK docs,"
        "within sdkdocs.zenml.io.",
        default=None,
    )
    is_custom: Optional[bool] = Field(
        title="Whether or not this flavor is a custom, user created flavor.",
        default=None,
    )
    workspace: Optional[UUID] = Field(
        title="The workspace to which this resource belongs.",
        default=None,
    )

`LoadedVisualization`

Bases: BaseModel

Model for loaded visualizations.

Source code in src/zenml/models/v2/misc/loaded_visualization.py

class LoadedVisualization(BaseModel):
    """Model for loaded visualizations."""

    type: VisualizationType
    value: Union[str, bytes] = Field(union_mode="left_to_right")

`LogsRequest`

Bases: BaseRequest

Request model for logs.

Source code in src/zenml/models/v2/core/logs.py

class LogsRequest(BaseRequest):
    """Request model for logs."""

    uri: str = Field(title="The uri of the logs file")

    artifact_store_id: Union[str, UUID] = Field(
        title="The artifact store ID to associate the logs with.",
        union_mode="left_to_right",
    )

    @field_validator("uri")
    @classmethod
    def text_field_max_length_check(cls, value: Any) -> Any:
        """Checks if the length of the value exceeds the maximum text length.

        Args:
            value: the value set in the field

        Returns:
            the value itself.

        Raises:
            AssertionError: if the length of the field is longer than the
                maximum threshold.
        """
        assert len(str(value)) < TEXT_FIELD_MAX_LENGTH, (
            "The length of the value for this field can not "
            f"exceed {TEXT_FIELD_MAX_LENGTH}"
        )
        return value

    @field_validator("artifact_store_id")
    @classmethod
    def str_field_max_length_check(cls, value: Any) -> Any:
        """Checks if the length of the value exceeds the maximum text length.

        Args:
            value: the value set in the field

        Returns:
            the value itself.

        Raises:
            AssertionError: if the length of the field is longer than the
                maximum threshold.
        """
        assert len(str(value)) < STR_FIELD_MAX_LENGTH, (
            "The length of the value for this field can not "
            f"exceed {STR_FIELD_MAX_LENGTH}"
        )
        return value

`str_field_max_length_check(value)` `classmethod`

Checks if the length of the value exceeds the maximum text length.

Parameters:

Name	Type	Description	Default
`value`	`Any`	the value set in the field	required

Returns:

Type	Description
`Any`	the value itself.

Raises:

Type	Description
`AssertionError`	if the length of the field is longer than the maximum threshold.

Source code in src/zenml/models/v2/core/logs.py

@field_validator("artifact_store_id")
@classmethod
def str_field_max_length_check(cls, value: Any) -> Any:
    """Checks if the length of the value exceeds the maximum text length.

    Args:
        value: the value set in the field

    Returns:
        the value itself.

    Raises:
        AssertionError: if the length of the field is longer than the
            maximum threshold.
    """
    assert len(str(value)) < STR_FIELD_MAX_LENGTH, (
        "The length of the value for this field can not "
        f"exceed {STR_FIELD_MAX_LENGTH}"
    )
    return value

`text_field_max_length_check(value)` `classmethod`

Checks if the length of the value exceeds the maximum text length.

Parameters:

Name	Type	Description	Default
`value`	`Any`	the value set in the field	required

Returns:

Type	Description
`Any`	the value itself.

Raises:

Type	Description
`AssertionError`	if the length of the field is longer than the maximum threshold.

Source code in src/zenml/models/v2/core/logs.py

@field_validator("uri")
@classmethod
def text_field_max_length_check(cls, value: Any) -> Any:
    """Checks if the length of the value exceeds the maximum text length.

    Args:
        value: the value set in the field

    Returns:
        the value itself.

    Raises:
        AssertionError: if the length of the field is longer than the
            maximum threshold.
    """
    assert len(str(value)) < TEXT_FIELD_MAX_LENGTH, (
        "The length of the value for this field can not "
        f"exceed {TEXT_FIELD_MAX_LENGTH}"
    )
    return value

`LogsResponse`

Bases: BaseIdentifiedResponse[LogsResponseBody, LogsResponseMetadata, LogsResponseResources]

Response model for logs.

Source code in src/zenml/models/v2/core/logs.py

class LogsResponse(
    BaseIdentifiedResponse[
        LogsResponseBody, LogsResponseMetadata, LogsResponseResources
    ]
):
    """Response model for logs."""

    def get_hydrated_version(self) -> "LogsResponse":
        """Get the hydrated version of these logs.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_logs(self.id)

    # Body and metadata properties
    @property
    def uri(self) -> str:
        """The `uri` property.

        Returns:
            the value of the property.
        """
        return self.get_body().uri

    @property
    def step_run_id(self) -> Optional[Union[str, UUID]]:
        """The `step_run_id` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().step_run_id

    @property
    def pipeline_run_id(self) -> Optional[Union[str, UUID]]:
        """The `pipeline_run_id` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().pipeline_run_id

    @property
    def artifact_store_id(self) -> Union[str, UUID]:
        """The `artifact_store_id` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().artifact_store_id

`artifact_store_id` `property`

The artifact_store_id property.

Returns:

Type	Description
`Union[str, UUID]`	the value of the property.

`pipeline_run_id` `property`

The pipeline_run_id property.

Returns:

Type	Description
`Optional[Union[str, UUID]]`	the value of the property.

`step_run_id` `property`

The step_run_id property.

Returns:

Type	Description
`Optional[Union[str, UUID]]`	the value of the property.

`uri` `property`

The uri property.

Returns:

Type	Description
`str`	the value of the property.

`get_hydrated_version()`

Get the hydrated version of these logs.

Returns:

Type	Description
`LogsResponse`	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/logs.py

def get_hydrated_version(self) -> "LogsResponse":
    """Get the hydrated version of these logs.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_logs(self.id)

`LogsResponseBody`

Response body for logs.

Source code in src/zenml/models/v2/core/logs.py

class LogsResponseBody(BaseDatedResponseBody):
    """Response body for logs."""

    uri: str = Field(
        title="The uri of the logs file",
        max_length=TEXT_FIELD_MAX_LENGTH,
    )

`LogsResponseMetadata`

Bases: BaseResponseMetadata

Response metadata for logs.

Source code in src/zenml/models/v2/core/logs.py

class LogsResponseMetadata(BaseResponseMetadata):
    """Response metadata for logs."""

    step_run_id: Optional[Union[str, UUID]] = Field(
        title="Step ID to associate the logs with.",
        default=None,
        description="When this is set, pipeline_run_id should be set to None.",
        union_mode="left_to_right",
    )
    pipeline_run_id: Optional[Union[str, UUID]] = Field(
        title="Pipeline run ID to associate the logs with.",
        default=None,
        description="When this is set, step_run_id should be set to None.",
        union_mode="left_to_right",
    )
    artifact_store_id: Union[str, UUID] = Field(
        title="The artifact store ID to associate the logs with.",
        union_mode="left_to_right",
    )

    @field_validator("artifact_store_id")
    @classmethod
    def str_field_max_length_check(cls, value: Any) -> Any:
        """Checks if the length of the value exceeds the maximum text length.

        Args:
            value: the value set in the field

        Returns:
            the value itself.

        Raises:
            AssertionError: if the length of the field is longer than the
                maximum threshold.
        """
        assert len(str(value)) < STR_FIELD_MAX_LENGTH
        return value

`str_field_max_length_check(value)` `classmethod`

Checks if the length of the value exceeds the maximum text length.

Parameters:

Name	Type	Description	Default
`value`	`Any`	the value set in the field	required

Returns:

Type	Description
`Any`	the value itself.

Raises:

Type	Description
`AssertionError`	if the length of the field is longer than the maximum threshold.

Source code in src/zenml/models/v2/core/logs.py

@field_validator("artifact_store_id")
@classmethod
def str_field_max_length_check(cls, value: Any) -> Any:
    """Checks if the length of the value exceeds the maximum text length.

    Args:
        value: the value set in the field

    Returns:
        the value itself.

    Raises:
        AssertionError: if the length of the field is longer than the
            maximum threshold.
    """
    assert len(str(value)) < STR_FIELD_MAX_LENGTH
    return value

`ModelFilter`

Model to enable advanced filtering of all Workspaces.

Source code in src/zenml/models/v2/core/model.py

class ModelFilter(WorkspaceScopedFilter, TaggableFilter):
    """Model to enable advanced filtering of all Workspaces."""

    name: Optional[str] = Field(
        default=None,
        description="Name of the Model",
    )

    FILTER_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        *WorkspaceScopedFilter.FILTER_EXCLUDE_FIELDS,
        *TaggableFilter.FILTER_EXCLUDE_FIELDS,
    ]
    CUSTOM_SORTING_OPTIONS: ClassVar[List[str]] = [
        *WorkspaceScopedFilter.CUSTOM_SORTING_OPTIONS,
        *TaggableFilter.CUSTOM_SORTING_OPTIONS,
        SORT_BY_LATEST_VERSION_KEY,
    ]
    CLI_EXCLUDE_FIELDS = [
        *WorkspaceScopedFilter.CLI_EXCLUDE_FIELDS,
        *TaggableFilter.CLI_EXCLUDE_FIELDS,
    ]

    def apply_sorting(
        self,
        query: AnyQuery,
        table: Type["AnySchema"],
    ) -> AnyQuery:
        """Apply sorting to the query for Models.

        Args:
            query: The query to which to apply the sorting.
            table: The query table.

        Returns:
            The query with sorting applied.
        """
        from sqlmodel import asc, case, col, desc, func, select

        from zenml.enums import SorterOps
        from zenml.zen_stores.schemas import (
            ModelSchema,
            ModelVersionSchema,
        )

        sort_by, operand = self.sorting_params

        if sort_by == SORT_BY_LATEST_VERSION_KEY:
            # Subquery to find the latest version per model
            latest_version_subquery = (
                select(
                    ModelSchema.id,
                    case(
                        (
                            func.max(ModelVersionSchema.created).is_(None),
                            ModelSchema.created,
                        ),
                        else_=func.max(ModelVersionSchema.created),
                    ).label("latest_version_created"),
                )
                .outerjoin(
                    ModelVersionSchema,
                    ModelSchema.id == ModelVersionSchema.model_id,  # type: ignore[arg-type]
                )
                .group_by(col(ModelSchema.id))
                .subquery()
            )

            query = query.add_columns(
                latest_version_subquery.c.latest_version_created,
            ).where(ModelSchema.id == latest_version_subquery.c.id)

            # Apply sorting based on the operand
            if operand == SorterOps.ASCENDING:
                query = query.order_by(
                    asc(latest_version_subquery.c.latest_version_created),
                    asc(ModelSchema.id),
                )
            else:
                query = query.order_by(
                    desc(latest_version_subquery.c.latest_version_created),
                    desc(ModelSchema.id),
                )
            return query

        # For other sorting cases, delegate to the parent class
        return super().apply_sorting(query=query, table=table)

`apply_sorting(query, table)`

Apply sorting to the query for Models.

Parameters:

Name	Type	Description	Default
`query`	`AnyQuery`	The query to which to apply the sorting.	required
`table`	`Type[AnySchema]`	The query table.	required

Returns:

Type	Description
`AnyQuery`	The query with sorting applied.

Source code in src/zenml/models/v2/core/model.py

def apply_sorting(
    self,
    query: AnyQuery,
    table: Type["AnySchema"],
) -> AnyQuery:
    """Apply sorting to the query for Models.

    Args:
        query: The query to which to apply the sorting.
        table: The query table.

    Returns:
        The query with sorting applied.
    """
    from sqlmodel import asc, case, col, desc, func, select

    from zenml.enums import SorterOps
    from zenml.zen_stores.schemas import (
        ModelSchema,
        ModelVersionSchema,
    )

    sort_by, operand = self.sorting_params

    if sort_by == SORT_BY_LATEST_VERSION_KEY:
        # Subquery to find the latest version per model
        latest_version_subquery = (
            select(
                ModelSchema.id,
                case(
                    (
                        func.max(ModelVersionSchema.created).is_(None),
                        ModelSchema.created,
                    ),
                    else_=func.max(ModelVersionSchema.created),
                ).label("latest_version_created"),
            )
            .outerjoin(
                ModelVersionSchema,
                ModelSchema.id == ModelVersionSchema.model_id,  # type: ignore[arg-type]
            )
            .group_by(col(ModelSchema.id))
            .subquery()
        )

        query = query.add_columns(
            latest_version_subquery.c.latest_version_created,
        ).where(ModelSchema.id == latest_version_subquery.c.id)

        # Apply sorting based on the operand
        if operand == SorterOps.ASCENDING:
            query = query.order_by(
                asc(latest_version_subquery.c.latest_version_created),
                asc(ModelSchema.id),
            )
        else:
            query = query.order_by(
                desc(latest_version_subquery.c.latest_version_created),
                desc(ModelSchema.id),
            )
        return query

    # For other sorting cases, delegate to the parent class
    return super().apply_sorting(query=query, table=table)

`ModelRequest`

Request model for models.

Source code in src/zenml/models/v2/core/model.py

class ModelRequest(WorkspaceScopedRequest):
    """Request model for models."""

    name: str = Field(
        title="The name of the model",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    license: Optional[str] = Field(
        title="The license model created under",
        max_length=TEXT_FIELD_MAX_LENGTH,
        default=None,
    )
    description: Optional[str] = Field(
        title="The description of the model",
        max_length=TEXT_FIELD_MAX_LENGTH,
        default=None,
    )
    audience: Optional[str] = Field(
        title="The target audience of the model",
        max_length=TEXT_FIELD_MAX_LENGTH,
        default=None,
    )
    use_cases: Optional[str] = Field(
        title="The use cases of the model",
        max_length=TEXT_FIELD_MAX_LENGTH,
        default=None,
    )
    limitations: Optional[str] = Field(
        title="The know limitations of the model",
        max_length=TEXT_FIELD_MAX_LENGTH,
        default=None,
    )
    trade_offs: Optional[str] = Field(
        title="The trade offs of the model",
        max_length=TEXT_FIELD_MAX_LENGTH,
        default=None,
    )
    ethics: Optional[str] = Field(
        title="The ethical implications of the model",
        max_length=TEXT_FIELD_MAX_LENGTH,
        default=None,
    )
    tags: Optional[List[str]] = Field(
        title="Tags associated with the model",
        default=None,
    )
    save_models_to_registry: bool = Field(
        title="Whether to save all ModelArtifacts to Model Registry",
        default=True,
    )

`ModelResponse`

Bases: WorkspaceScopedResponse[ModelResponseBody, ModelResponseMetadata, ModelResponseResources]

Response model for models.

Source code in src/zenml/models/v2/core/model.py

class ModelResponse(
    WorkspaceScopedResponse[
        ModelResponseBody, ModelResponseMetadata, ModelResponseResources
    ]
):
    """Response model for models."""

    name: str = Field(
        title="The name of the model",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    def get_hydrated_version(self) -> "ModelResponse":
        """Get the hydrated version of this model.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_model(self.id)

    # Body and metadata properties
    @property
    def tags(self) -> List["TagResponse"]:
        """The `tags` property.

        Returns:
            the value of the property.
        """
        return self.get_body().tags

    @property
    def latest_version_name(self) -> Optional[str]:
        """The `latest_version_name` property.

        Returns:
            the value of the property.
        """
        return self.get_body().latest_version_name

    @property
    def latest_version_id(self) -> Optional[UUID]:
        """The `latest_version_id` property.

        Returns:
            the value of the property.
        """
        return self.get_body().latest_version_id

    @property
    def license(self) -> Optional[str]:
        """The `license` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().license

    @property
    def description(self) -> Optional[str]:
        """The `description` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().description

    @property
    def audience(self) -> Optional[str]:
        """The `audience` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().audience

    @property
    def use_cases(self) -> Optional[str]:
        """The `use_cases` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().use_cases

    @property
    def limitations(self) -> Optional[str]:
        """The `limitations` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().limitations

    @property
    def trade_offs(self) -> Optional[str]:
        """The `trade_offs` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().trade_offs

    @property
    def ethics(self) -> Optional[str]:
        """The `ethics` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().ethics

    @property
    def save_models_to_registry(self) -> bool:
        """The `save_models_to_registry` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().save_models_to_registry

    # Helper functions
    @property
    def versions(self) -> List["Model"]:
        """List all versions of the model.

        Returns:
            The list of all model version.
        """
        from zenml.client import Client

        client = Client()
        model_versions = depaginate(
            client.list_model_versions, model_name_or_id=self.id
        )
        return [
            mv.to_model_class(suppress_class_validation_warnings=True)
            for mv in model_versions
        ]

`audience` `property`

The audience property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`description` `property`

The description property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`ethics` `property`

The ethics property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`latest_version_id` `property`

The latest_version_id property.

Returns:

Type	Description
`Optional[UUID]`	the value of the property.

`latest_version_name` `property`

The latest_version_name property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`license` `property`

The license property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`limitations` `property`

The limitations property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`save_models_to_registry` `property`

The save_models_to_registry property.

Returns:

Type	Description
`bool`	the value of the property.

`tags` `property`

The tags property.

Returns:

Type	Description
`List[TagResponse]`	the value of the property.

`trade_offs` `property`

The trade_offs property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`use_cases` `property`

The use_cases property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`versions` `property`

List all versions of the model.

Returns:

Type	Description
`List[Model]`	The list of all model version.

`get_hydrated_version()`

Get the hydrated version of this model.

Returns:

Type	Description
`ModelResponse`	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/model.py

def get_hydrated_version(self) -> "ModelResponse":
    """Get the hydrated version of this model.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_model(self.id)

`ModelResponseBody`

Response body for models.

Source code in src/zenml/models/v2/core/model.py

class ModelResponseBody(WorkspaceScopedResponseBody):
    """Response body for models."""

    tags: List["TagResponse"] = Field(
        title="Tags associated with the model",
    )
    latest_version_name: Optional[str] = None
    latest_version_id: Optional[UUID] = None

`ModelResponseMetadata`

Response metadata for models.

Source code in src/zenml/models/v2/core/model.py

class ModelResponseMetadata(WorkspaceScopedResponseMetadata):
    """Response metadata for models."""

    license: Optional[str] = Field(
        title="The license model created under",
        max_length=TEXT_FIELD_MAX_LENGTH,
        default=None,
    )
    description: Optional[str] = Field(
        title="The description of the model",
        max_length=TEXT_FIELD_MAX_LENGTH,
        default=None,
    )
    audience: Optional[str] = Field(
        title="The target audience of the model",
        max_length=TEXT_FIELD_MAX_LENGTH,
        default=None,
    )
    use_cases: Optional[str] = Field(
        title="The use cases of the model",
        max_length=TEXT_FIELD_MAX_LENGTH,
        default=None,
    )
    limitations: Optional[str] = Field(
        title="The know limitations of the model",
        max_length=TEXT_FIELD_MAX_LENGTH,
        default=None,
    )
    trade_offs: Optional[str] = Field(
        title="The trade offs of the model",
        max_length=TEXT_FIELD_MAX_LENGTH,
        default=None,
    )
    ethics: Optional[str] = Field(
        title="The ethical implications of the model",
        max_length=TEXT_FIELD_MAX_LENGTH,
        default=None,
    )
    save_models_to_registry: bool = Field(
        title="Whether to save all ModelArtifacts to Model Registry",
        default=True,
    )

`ModelUpdate`

Bases: BaseModel

Update model for models.

Source code in src/zenml/models/v2/core/model.py

class ModelUpdate(BaseModel):
    """Update model for models."""

    name: Optional[str] = None
    license: Optional[str] = None
    description: Optional[str] = None
    audience: Optional[str] = None
    use_cases: Optional[str] = None
    limitations: Optional[str] = None
    trade_offs: Optional[str] = None
    ethics: Optional[str] = None
    add_tags: Optional[List[str]] = None
    remove_tags: Optional[List[str]] = None
    save_models_to_registry: Optional[bool] = None

`ModelVersionArtifactFilter`

Bases: BaseFilter

Model version pipeline run links filter model.

Source code in src/zenml/models/v2/core/model_version_artifact.py

class ModelVersionArtifactFilter(BaseFilter):
    """Model version pipeline run links filter model."""

    # Artifact name and type are not DB fields and need to be handled separately
    FILTER_EXCLUDE_FIELDS = [
        *BaseFilter.FILTER_EXCLUDE_FIELDS,
        "artifact_name",
        "only_data_artifacts",
        "only_model_artifacts",
        "only_deployment_artifacts",
        "has_custom_name",
        "user",
    ]
    CLI_EXCLUDE_FIELDS = [
        *BaseFilter.CLI_EXCLUDE_FIELDS,
        "only_data_artifacts",
        "only_model_artifacts",
        "only_deployment_artifacts",
        "has_custom_name",
        "model_version_id",
        "updated",
        "id",
    ]

    model_version_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Filter by model version ID",
        union_mode="left_to_right",
    )
    artifact_version_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Filter by artifact ID",
        union_mode="left_to_right",
    )
    artifact_name: Optional[str] = Field(
        default=None,
        description="Name of the artifact",
    )
    only_data_artifacts: Optional[bool] = False
    only_model_artifacts: Optional[bool] = False
    only_deployment_artifacts: Optional[bool] = False
    has_custom_name: Optional[bool] = None
    user: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Name/ID of the user that created the artifact.",
    )

    # TODO: In Pydantic v2, the `model_` is a protected namespaces for all
    #  fields defined under base models. If not handled, this raises a warning.
    #  It is possible to suppress this warning message with the following
    #  configuration, however the ultimate solution is to rename these fields.
    #  Even though they do not cause any problems right now, if we are not
    #  careful we might overwrite some fields protected by pydantic.
    model_config = ConfigDict(protected_namespaces=())

    def get_custom_filters(
        self, table: Type["AnySchema"]
    ) -> List[Union["ColumnElement[bool]"]]:
        """Get custom filters.

        Args:
            table: The query table.

        Returns:
            A list of custom filters.
        """
        custom_filters = super().get_custom_filters(table)

        from sqlmodel import and_, col

        from zenml.zen_stores.schemas import (
            ArtifactSchema,
            ArtifactVersionSchema,
            ModelVersionArtifactSchema,
            UserSchema,
        )

        if self.artifact_name:
            value, filter_operator = self._resolve_operator(self.artifact_name)
            filter_ = StrFilter(
                operation=GenericFilterOps(filter_operator),
                column="name",
                value=value,
            )
            artifact_name_filter = and_(
                ModelVersionArtifactSchema.artifact_version_id
                == ArtifactVersionSchema.id,
                ArtifactVersionSchema.artifact_id == ArtifactSchema.id,
                filter_.generate_query_conditions(ArtifactSchema),
            )
            custom_filters.append(artifact_name_filter)

        if self.only_data_artifacts:
            data_artifact_filter = and_(
                ModelVersionArtifactSchema.artifact_version_id
                == ArtifactVersionSchema.id,
                col(ArtifactVersionSchema.type).not_in(
                    ["ServiceArtifact", "ModelArtifact"]
                ),
            )
            custom_filters.append(data_artifact_filter)

        if self.only_model_artifacts:
            model_artifact_filter = and_(
                ModelVersionArtifactSchema.artifact_version_id
                == ArtifactVersionSchema.id,
                ArtifactVersionSchema.type == "ModelArtifact",
            )
            custom_filters.append(model_artifact_filter)

        if self.only_deployment_artifacts:
            deployment_artifact_filter = and_(
                ModelVersionArtifactSchema.artifact_version_id
                == ArtifactVersionSchema.id,
                ArtifactVersionSchema.type == "ServiceArtifact",
            )
            custom_filters.append(deployment_artifact_filter)

        if self.has_custom_name is not None:
            custom_name_filter = and_(
                ModelVersionArtifactSchema.artifact_version_id
                == ArtifactVersionSchema.id,
                ArtifactVersionSchema.artifact_id == ArtifactSchema.id,
                ArtifactSchema.has_custom_name == self.has_custom_name,
            )
            custom_filters.append(custom_name_filter)

        if self.user:
            user_filter = and_(
                ModelVersionArtifactSchema.artifact_version_id
                == ArtifactVersionSchema.id,
                ArtifactVersionSchema.user_id == UserSchema.id,
                self.generate_name_or_id_query_conditions(
                    value=self.user,
                    table=UserSchema,
                    additional_columns=["full_name"],
                ),
            )
            custom_filters.append(user_filter)

        return custom_filters

`get_custom_filters(table)`

Get custom filters.

Parameters:

Name	Type	Description	Default
`table`	`Type[AnySchema]`	The query table.	required

Returns:

Type	Description
`List[Union[ColumnElement[bool]]]`	A list of custom filters.

Source code in src/zenml/models/v2/core/model_version_artifact.py

def get_custom_filters(
    self, table: Type["AnySchema"]
) -> List[Union["ColumnElement[bool]"]]:
    """Get custom filters.

    Args:
        table: The query table.

    Returns:
        A list of custom filters.
    """
    custom_filters = super().get_custom_filters(table)

    from sqlmodel import and_, col

    from zenml.zen_stores.schemas import (
        ArtifactSchema,
        ArtifactVersionSchema,
        ModelVersionArtifactSchema,
        UserSchema,
    )

    if self.artifact_name:
        value, filter_operator = self._resolve_operator(self.artifact_name)
        filter_ = StrFilter(
            operation=GenericFilterOps(filter_operator),
            column="name",
            value=value,
        )
        artifact_name_filter = and_(
            ModelVersionArtifactSchema.artifact_version_id
            == ArtifactVersionSchema.id,
            ArtifactVersionSchema.artifact_id == ArtifactSchema.id,
            filter_.generate_query_conditions(ArtifactSchema),
        )
        custom_filters.append(artifact_name_filter)

    if self.only_data_artifacts:
        data_artifact_filter = and_(
            ModelVersionArtifactSchema.artifact_version_id
            == ArtifactVersionSchema.id,
            col(ArtifactVersionSchema.type).not_in(
                ["ServiceArtifact", "ModelArtifact"]
            ),
        )
        custom_filters.append(data_artifact_filter)

    if self.only_model_artifacts:
        model_artifact_filter = and_(
            ModelVersionArtifactSchema.artifact_version_id
            == ArtifactVersionSchema.id,
            ArtifactVersionSchema.type == "ModelArtifact",
        )
        custom_filters.append(model_artifact_filter)

    if self.only_deployment_artifacts:
        deployment_artifact_filter = and_(
            ModelVersionArtifactSchema.artifact_version_id
            == ArtifactVersionSchema.id,
            ArtifactVersionSchema.type == "ServiceArtifact",
        )
        custom_filters.append(deployment_artifact_filter)

    if self.has_custom_name is not None:
        custom_name_filter = and_(
            ModelVersionArtifactSchema.artifact_version_id
            == ArtifactVersionSchema.id,
            ArtifactVersionSchema.artifact_id == ArtifactSchema.id,
            ArtifactSchema.has_custom_name == self.has_custom_name,
        )
        custom_filters.append(custom_name_filter)

    if self.user:
        user_filter = and_(
            ModelVersionArtifactSchema.artifact_version_id
            == ArtifactVersionSchema.id,
            ArtifactVersionSchema.user_id == UserSchema.id,
            self.generate_name_or_id_query_conditions(
                value=self.user,
                table=UserSchema,
                additional_columns=["full_name"],
            ),
        )
        custom_filters.append(user_filter)

    return custom_filters

`ModelVersionArtifactRequest`

Bases: BaseRequest

Request model for links between model versions and artifacts.

Source code in src/zenml/models/v2/core/model_version_artifact.py

class ModelVersionArtifactRequest(BaseRequest):
    """Request model for links between model versions and artifacts."""

    model_version: UUID
    artifact_version: UUID

    # TODO: In Pydantic v2, the `model_` is a protected namespaces for all
    #  fields defined under base models. If not handled, this raises a warning.
    #  It is possible to suppress this warning message with the following
    #  configuration, however the ultimate solution is to rename these fields.
    #  Even though they do not cause any problems right now, if we are not
    #  careful we might overwrite some fields protected by pydantic.
    model_config = ConfigDict(protected_namespaces=())

`ModelVersionArtifactResponse`

Bases: BaseIdentifiedResponse[ModelVersionArtifactResponseBody, BaseResponseMetadata, ModelVersionArtifactResponseResources]

Response model for links between model versions and artifacts.

Source code in src/zenml/models/v2/core/model_version_artifact.py

class ModelVersionArtifactResponse(
    BaseIdentifiedResponse[
        ModelVersionArtifactResponseBody,
        BaseResponseMetadata,
        ModelVersionArtifactResponseResources,
    ]
):
    """Response model for links between model versions and artifacts."""

    @property
    def model_version(self) -> UUID:
        """The `model_version` property.

        Returns:
            the value of the property.
        """
        return self.get_body().model_version

    @property
    def artifact_version(self) -> "ArtifactVersionResponse":
        """The `artifact_version` property.

        Returns:
            the value of the property.
        """
        return self.get_body().artifact_version

`artifact_version` `property`

The artifact_version property.

Returns:

Type	Description
`ArtifactVersionResponse`	the value of the property.

`model_version` `property`

The model_version property.

Returns:

Type	Description
`UUID`	the value of the property.

`ModelVersionArtifactResponseBody`

Response body for links between model versions and artifacts.

Source code in src/zenml/models/v2/core/model_version_artifact.py

class ModelVersionArtifactResponseBody(BaseDatedResponseBody):
    """Response body for links between model versions and artifacts."""

    model_version: UUID
    artifact_version: "ArtifactVersionResponse"

    # TODO: In Pydantic v2, the `model_` is a protected namespaces for all
    #  fields defined under base models. If not handled, this raises a warning.
    #  It is possible to suppress this warning message with the following
    #  configuration, however the ultimate solution is to rename these fields.
    #  Even though they do not cause any problems right now, if we are not
    #  careful we might overwrite some fields protected by pydantic.
    model_config = ConfigDict(protected_namespaces=())

`ModelVersionFilter`

Filter model for model versions.

Source code in src/zenml/models/v2/core/model_version.py

class ModelVersionFilter(WorkspaceScopedFilter, TaggableFilter):
    """Filter model for model versions."""

    FILTER_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        *WorkspaceScopedFilter.FILTER_EXCLUDE_FIELDS,
        *TaggableFilter.FILTER_EXCLUDE_FIELDS,
        "run_metadata",
    ]
    CUSTOM_SORTING_OPTIONS = [
        *WorkspaceScopedFilter.CUSTOM_SORTING_OPTIONS,
        *TaggableFilter.CUSTOM_SORTING_OPTIONS,
    ]
    CLI_EXCLUDE_FIELDS = [
        *WorkspaceScopedFilter.CLI_EXCLUDE_FIELDS,
        *TaggableFilter.CLI_EXCLUDE_FIELDS,
    ]

    name: Optional[str] = Field(
        default=None,
        description="The name of the Model Version",
    )
    number: Optional[int] = Field(
        default=None,
        description="The number of the Model Version",
    )
    stage: Optional[Union[str, ModelStages]] = Field(
        description="The model version stage",
        default=None,
        union_mode="left_to_right",
    )
    run_metadata: Optional[Dict[str, str]] = Field(
        default=None,
        description="The run_metadata to filter the model versions by.",
    )

    _model_id: UUID = PrivateAttr(None)

    def set_scope_model(self, model_name_or_id: Union[str, UUID]) -> None:
        """Set the model to scope this response.

        Args:
            model_name_or_id: The model to scope this response to.
        """
        try:
            model_id = UUID(str(model_name_or_id))
        except ValueError:
            from zenml.client import Client

            model_id = Client().get_model(model_name_or_id).id

        self._model_id = model_id

    def get_custom_filters(
        self, table: Type["AnySchema"]
    ) -> List["ColumnElement[bool]"]:
        """Get custom filters.

        Args:
            table: The query table.

        Returns:
            A list of custom filters.
        """
        custom_filters = super().get_custom_filters(table)

        from sqlmodel import and_

        from zenml.zen_stores.schemas import (
            ModelVersionSchema,
            RunMetadataResourceSchema,
            RunMetadataSchema,
        )

        if self.run_metadata is not None:
            from zenml.enums import MetadataResourceTypes

            for key, value in self.run_metadata.items():
                additional_filter = and_(
                    RunMetadataResourceSchema.resource_id
                    == ModelVersionSchema.id,
                    RunMetadataResourceSchema.resource_type
                    == MetadataResourceTypes.MODEL_VERSION,
                    RunMetadataResourceSchema.run_metadata_id
                    == RunMetadataSchema.id,
                    self.generate_custom_query_conditions_for_column(
                        value=value,
                        table=RunMetadataSchema,
                        column="value",
                    ),
                )
                custom_filters.append(additional_filter)

        return custom_filters

    def apply_filter(
        self,
        query: AnyQuery,
        table: Type["AnySchema"],
    ) -> AnyQuery:
        """Applies the filter to a query.

        Args:
            query: The query to which to apply the filter.
            table: The query table.

        Returns:
            The query with filter applied.
        """
        query = super().apply_filter(query=query, table=table)

        if self._model_id:
            query = query.where(getattr(table, "model_id") == self._model_id)

        return query

`apply_filter(query, table)`

Applies the filter to a query.

Parameters:

Name	Type	Description	Default
`query`	`AnyQuery`	The query to which to apply the filter.	required
`table`	`Type[AnySchema]`	The query table.	required

Returns:

Type	Description
`AnyQuery`	The query with filter applied.

Source code in src/zenml/models/v2/core/model_version.py

def apply_filter(
    self,
    query: AnyQuery,
    table: Type["AnySchema"],
) -> AnyQuery:
    """Applies the filter to a query.

    Args:
        query: The query to which to apply the filter.
        table: The query table.

    Returns:
        The query with filter applied.
    """
    query = super().apply_filter(query=query, table=table)

    if self._model_id:
        query = query.where(getattr(table, "model_id") == self._model_id)

    return query

`get_custom_filters(table)`

Get custom filters.

Parameters:

Name	Type	Description	Default
`table`	`Type[AnySchema]`	The query table.	required

Returns:

Type	Description
`List[ColumnElement[bool]]`	A list of custom filters.

Source code in src/zenml/models/v2/core/model_version.py

def get_custom_filters(
    self, table: Type["AnySchema"]
) -> List["ColumnElement[bool]"]:
    """Get custom filters.

    Args:
        table: The query table.

    Returns:
        A list of custom filters.
    """
    custom_filters = super().get_custom_filters(table)

    from sqlmodel import and_

    from zenml.zen_stores.schemas import (
        ModelVersionSchema,
        RunMetadataResourceSchema,
        RunMetadataSchema,
    )

    if self.run_metadata is not None:
        from zenml.enums import MetadataResourceTypes

        for key, value in self.run_metadata.items():
            additional_filter = and_(
                RunMetadataResourceSchema.resource_id
                == ModelVersionSchema.id,
                RunMetadataResourceSchema.resource_type
                == MetadataResourceTypes.MODEL_VERSION,
                RunMetadataResourceSchema.run_metadata_id
                == RunMetadataSchema.id,
                self.generate_custom_query_conditions_for_column(
                    value=value,
                    table=RunMetadataSchema,
                    column="value",
                ),
            )
            custom_filters.append(additional_filter)

    return custom_filters

`set_scope_model(model_name_or_id)`

Set the model to scope this response.

Parameters:

Name	Type	Description	Default
`model_name_or_id`	`Union[str, UUID]`	The model to scope this response to.	required

Source code in src/zenml/models/v2/core/model_version.py

def set_scope_model(self, model_name_or_id: Union[str, UUID]) -> None:
    """Set the model to scope this response.

    Args:
        model_name_or_id: The model to scope this response to.
    """
    try:
        model_id = UUID(str(model_name_or_id))
    except ValueError:
        from zenml.client import Client

        model_id = Client().get_model(model_name_or_id).id

    self._model_id = model_id

`ModelVersionPipelineRunFilter`

Bases: BaseFilter

Model version pipeline run links filter model.

Source code in src/zenml/models/v2/core/model_version_pipeline_run.py

class ModelVersionPipelineRunFilter(BaseFilter):
    """Model version pipeline run links filter model."""

    FILTER_EXCLUDE_FIELDS = [
        *BaseFilter.FILTER_EXCLUDE_FIELDS,
        "pipeline_run_name",
        "user",
    ]
    CLI_EXCLUDE_FIELDS = [
        *BaseFilter.CLI_EXCLUDE_FIELDS,
        "model_version_id",
        "updated",
        "id",
    ]

    model_version_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Filter by model version ID",
        union_mode="left_to_right",
    )
    pipeline_run_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Filter by pipeline run ID",
        union_mode="left_to_right",
    )
    pipeline_run_name: Optional[str] = Field(
        default=None,
        description="Name of the pipeline run",
    )
    user: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Name/ID of the user that created the pipeline run.",
    )

    # TODO: In Pydantic v2, the `model_` is a protected namespaces for all
    #  fields defined under base models. If not handled, this raises a warning.
    #  It is possible to suppress this warning message with the following
    #  configuration, however the ultimate solution is to rename these fields.
    #  Even though they do not cause any problems right now, if we are not
    #  careful we might overwrite some fields protected by pydantic.
    model_config = ConfigDict(protected_namespaces=())

    def get_custom_filters(
        self, table: Type["AnySchema"]
    ) -> List["ColumnElement[bool]"]:
        """Get custom filters.

        Args:
            table: The query table.

        Returns:
            A list of custom filters.
        """
        custom_filters = super().get_custom_filters(table)

        from sqlmodel import and_

        from zenml.zen_stores.schemas import (
            ModelVersionPipelineRunSchema,
            PipelineRunSchema,
            UserSchema,
        )

        if self.pipeline_run_name:
            value, filter_operator = self._resolve_operator(
                self.pipeline_run_name
            )
            filter_ = StrFilter(
                operation=GenericFilterOps(filter_operator),
                column="name",
                value=value,
            )
            pipeline_run_name_filter = and_(
                ModelVersionPipelineRunSchema.pipeline_run_id
                == PipelineRunSchema.id,
                filter_.generate_query_conditions(PipelineRunSchema),
            )
            custom_filters.append(pipeline_run_name_filter)

        if self.user:
            user_filter = and_(
                ModelVersionPipelineRunSchema.pipeline_run_id
                == PipelineRunSchema.id,
                PipelineRunSchema.user_id == UserSchema.id,
                self.generate_name_or_id_query_conditions(
                    value=self.user,
                    table=UserSchema,
                    additional_columns=["full_name"],
                ),
            )
            custom_filters.append(user_filter)

        return custom_filters

`get_custom_filters(table)`

Get custom filters.

Parameters:

Name	Type	Description	Default
`table`	`Type[AnySchema]`	The query table.	required

Returns:

Type	Description
`List[ColumnElement[bool]]`	A list of custom filters.

Source code in src/zenml/models/v2/core/model_version_pipeline_run.py

def get_custom_filters(
    self, table: Type["AnySchema"]
) -> List["ColumnElement[bool]"]:
    """Get custom filters.

    Args:
        table: The query table.

    Returns:
        A list of custom filters.
    """
    custom_filters = super().get_custom_filters(table)

    from sqlmodel import and_

    from zenml.zen_stores.schemas import (
        ModelVersionPipelineRunSchema,
        PipelineRunSchema,
        UserSchema,
    )

    if self.pipeline_run_name:
        value, filter_operator = self._resolve_operator(
            self.pipeline_run_name
        )
        filter_ = StrFilter(
            operation=GenericFilterOps(filter_operator),
            column="name",
            value=value,
        )
        pipeline_run_name_filter = and_(
            ModelVersionPipelineRunSchema.pipeline_run_id
            == PipelineRunSchema.id,
            filter_.generate_query_conditions(PipelineRunSchema),
        )
        custom_filters.append(pipeline_run_name_filter)

    if self.user:
        user_filter = and_(
            ModelVersionPipelineRunSchema.pipeline_run_id
            == PipelineRunSchema.id,
            PipelineRunSchema.user_id == UserSchema.id,
            self.generate_name_or_id_query_conditions(
                value=self.user,
                table=UserSchema,
                additional_columns=["full_name"],
            ),
        )
        custom_filters.append(user_filter)

    return custom_filters

`ModelVersionPipelineRunRequest`

Bases: BaseRequest

Request model for links between model versions and pipeline runs.

Source code in src/zenml/models/v2/core/model_version_pipeline_run.py

class ModelVersionPipelineRunRequest(BaseRequest):
    """Request model for links between model versions and pipeline runs."""

    model_version: UUID
    pipeline_run: UUID

    # TODO: In Pydantic v2, the `model_` is a protected namespaces for all
    #  fields defined under base models. If not handled, this raises a warning.
    #  It is possible to suppress this warning message with the following
    #  configuration, however the ultimate solution is to rename these fields.
    #  Even though they do not cause any problems right now, if we are not
    #  careful we might overwrite some fields protected by pydantic.
    model_config = ConfigDict(protected_namespaces=())

`ModelVersionPipelineRunResponse`

Bases: BaseIdentifiedResponse[ModelVersionPipelineRunResponseBody, BaseResponseMetadata, ModelVersionPipelineRunResponseResources]

Response model for links between model versions and pipeline runs.

Source code in src/zenml/models/v2/core/model_version_pipeline_run.py

class ModelVersionPipelineRunResponse(
    BaseIdentifiedResponse[
        ModelVersionPipelineRunResponseBody,
        BaseResponseMetadata,
        ModelVersionPipelineRunResponseResources,
    ]
):
    """Response model for links between model versions and pipeline runs."""

    @property
    def model_version(self) -> UUID:
        """The `model_version` property.

        Returns:
            the value of the property.
        """
        return self.get_body().model_version

    @property
    def pipeline_run(self) -> "PipelineRunResponse":
        """The `pipeline_run` property.

        Returns:
            the value of the property.
        """
        return self.get_body().pipeline_run

`model_version` `property`

The model_version property.

Returns:

Type	Description
`UUID`	the value of the property.

`pipeline_run` `property`

The pipeline_run property.

Returns:

Type	Description
`PipelineRunResponse`	the value of the property.

`ModelVersionPipelineRunResponseBody`

Response body for links between model versions and pipeline runs.

Source code in src/zenml/models/v2/core/model_version_pipeline_run.py

class ModelVersionPipelineRunResponseBody(BaseDatedResponseBody):
    """Response body for links between model versions and pipeline runs."""

    model_version: UUID
    pipeline_run: PipelineRunResponse

    # TODO: In Pydantic v2, the `model_` is a protected namespaces for all
    #  fields defined under base models. If not handled, this raises a warning.
    #  It is possible to suppress this warning message with the following
    #  configuration, however the ultimate solution is to rename these fields.
    #  Even though they do not cause any problems right now, if we are not
    #  careful we might overwrite some fields protected by pydantic.
    model_config = ConfigDict(protected_namespaces=())

`ModelVersionRequest`

Request model for model versions.

Source code in src/zenml/models/v2/core/model_version.py

class ModelVersionRequest(WorkspaceScopedRequest):
    """Request model for model versions."""

    name: Optional[str] = Field(
        description="The name of the model version",
        max_length=STR_FIELD_MAX_LENGTH,
        default=None,
    )
    description: Optional[str] = Field(
        description="The description of the model version",
        max_length=TEXT_FIELD_MAX_LENGTH,
        default=None,
    )
    stage: Optional[str] = Field(
        description="The stage of the model version",
        max_length=STR_FIELD_MAX_LENGTH,
        default=None,
    )

    model: UUID = Field(
        description="The ID of the model containing version",
    )
    tags: Optional[List[str]] = Field(
        title="Tags associated with the model version",
        default=None,
    )

`ModelVersionResponse`

Bases: WorkspaceScopedResponse[ModelVersionResponseBody, ModelVersionResponseMetadata, ModelVersionResponseResources]

Response model for model versions.

Source code in src/zenml/models/v2/core/model_version.py

class ModelVersionResponse(
    WorkspaceScopedResponse[
        ModelVersionResponseBody,
        ModelVersionResponseMetadata,
        ModelVersionResponseResources,
    ]
):
    """Response model for model versions."""

    name: Optional[str] = Field(
        description="The name of the model version",
        max_length=STR_FIELD_MAX_LENGTH,
        default=None,
    )

    @property
    def stage(self) -> Optional[str]:
        """The `stage` property.

        Returns:
            the value of the property.
        """
        return self.get_body().stage

    @property
    def number(self) -> int:
        """The `number` property.

        Returns:
            the value of the property.
        """
        return self.get_body().number

    @property
    def model(self) -> "ModelResponse":
        """The `model` property.

        Returns:
            the value of the property.
        """
        return self.get_body().model

    @property
    def model_artifact_ids(self) -> Dict[str, Dict[str, UUID]]:
        """The `model_artifact_ids` property.

        Returns:
            the value of the property.
        """
        return self.get_body().model_artifact_ids

    @property
    def data_artifact_ids(self) -> Dict[str, Dict[str, UUID]]:
        """The `data_artifact_ids` property.

        Returns:
            the value of the property.
        """
        return self.get_body().data_artifact_ids

    @property
    def deployment_artifact_ids(self) -> Dict[str, Dict[str, UUID]]:
        """The `deployment_artifact_ids` property.

        Returns:
            the value of the property.
        """
        return self.get_body().deployment_artifact_ids

    @property
    def pipeline_run_ids(self) -> Dict[str, UUID]:
        """The `pipeline_run_ids` property.

        Returns:
            the value of the property.
        """
        return self.get_body().pipeline_run_ids

    @property
    def tags(self) -> List[TagResponse]:
        """The `tags` property.

        Returns:
            the value of the property.
        """
        return self.get_body().tags

    @property
    def description(self) -> Optional[str]:
        """The `description` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().description

    @property
    def run_metadata(self) -> Dict[str, MetadataType]:
        """The `run_metadata` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().run_metadata

    def get_hydrated_version(self) -> "ModelVersionResponse":
        """Get the hydrated version of this model version.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_model_version(self.id)

    # Helper functions
    def to_model_class(
        self,
        suppress_class_validation_warnings: bool = True,
    ) -> "Model":
        """Convert response model to Model object.

        Args:
            suppress_class_validation_warnings: internally used to suppress
                repeated warnings.

        Returns:
            Model object
        """
        from zenml.model.model import Model

        mv = Model(
            name=self.model.name,
            license=self.model.license,
            description=self.description,
            audience=self.model.audience,
            use_cases=self.model.use_cases,
            limitations=self.model.limitations,
            trade_offs=self.model.trade_offs,
            ethics=self.model.ethics,
            tags=[t.name for t in self.tags],
            version=self.name,
            suppress_class_validation_warnings=suppress_class_validation_warnings,
            model_version_id=self.id,
        )

        return mv

    @property
    def model_artifacts(
        self,
    ) -> Dict[str, Dict[str, "ArtifactVersionResponse"]]:
        """Get all model artifacts linked to this model version.

        Returns:
            Dictionary of model artifacts with versions as
            Dict[str, Dict[str, ArtifactResponse]]
        """
        from zenml.client import Client

        return {
            name: {
                version: Client().get_artifact_version(a)
                for version, a in self.model_artifact_ids[name].items()
            }
            for name in self.model_artifact_ids
        }

    @property
    def data_artifacts(
        self,
    ) -> Dict[str, Dict[str, "ArtifactVersionResponse"]]:
        """Get all data artifacts linked to this model version.

        Returns:
            Dictionary of data artifacts with versions as
            Dict[str, Dict[str, ArtifactResponse]]
        """
        from zenml.client import Client

        return {
            name: {
                version: Client().get_artifact_version(a)
                for version, a in self.data_artifact_ids[name].items()
            }
            for name in self.data_artifact_ids
        }

    @property
    def deployment_artifacts(
        self,
    ) -> Dict[str, Dict[str, "ArtifactVersionResponse"]]:
        """Get all deployment artifacts linked to this model version.

        Returns:
            Dictionary of deployment artifacts with versions as
            Dict[str, Dict[str, ArtifactResponse]]
        """
        from zenml.client import Client

        return {
            name: {
                version: Client().get_artifact_version(a)
                for version, a in self.deployment_artifact_ids[name].items()
            }
            for name in self.deployment_artifact_ids
        }

    @property
    def pipeline_runs(self) -> Dict[str, "PipelineRunResponse"]:
        """Get all pipeline runs linked to this version.

        Returns:
            Dictionary of Pipeline Runs as PipelineRunResponseModel
        """
        from zenml.client import Client

        return {
            name: Client().get_pipeline_run(pr)
            for name, pr in self.pipeline_run_ids.items()
        }

    def _get_linked_object(
        self,
        name: str,
        version: Optional[str] = None,
        type: Optional[ArtifactType] = None,
    ) -> Optional["ArtifactVersionResponse"]:
        """Get the artifact linked to this model version given type.

        Args:
            name: The name of the artifact to retrieve.
            version: The version of the artifact to retrieve (None for
                latest/non-versioned)
            type: The type of the artifact to filter by.

        Returns:
            Specific version of an artifact from collection or None
        """
        from zenml.client import Client

        artifact_versions = Client().list_artifact_versions(
            sort_by="desc:created",
            size=1,
            name=name,
            version=version,
            model_version_id=self.id,
            type=type,
            hydrate=True,
        )

        if not artifact_versions.items:
            return None
        return artifact_versions.items[0]

    def get_artifact(
        self,
        name: str,
        version: Optional[str] = None,
    ) -> Optional["ArtifactVersionResponse"]:
        """Get the artifact linked to this model version.

        Args:
            name: The name of the artifact to retrieve.
            version: The version of the artifact to retrieve (None for
                latest/non-versioned)

        Returns:
            Specific version of an artifact or None
        """
        return self._get_linked_object(name, version)

    def get_model_artifact(
        self,
        name: str,
        version: Optional[str] = None,
    ) -> Optional["ArtifactVersionResponse"]:
        """Get the model artifact linked to this model version.

        Args:
            name: The name of the model artifact to retrieve.
            version: The version of the model artifact to retrieve (None for
                latest/non-versioned)

        Returns:
            Specific version of the model artifact or None
        """
        return self._get_linked_object(name, version, ArtifactType.MODEL)

    def get_data_artifact(
        self,
        name: str,
        version: Optional[str] = None,
    ) -> Optional["ArtifactVersionResponse"]:
        """Get the data artifact linked to this model version.

        Args:
            name: The name of the data artifact to retrieve.
            version: The version of the data artifact to retrieve (None for
                latest/non-versioned)

        Returns:
            Specific version of the data artifact or None
        """
        return self._get_linked_object(name, version, ArtifactType.DATA)

    def get_deployment_artifact(
        self,
        name: str,
        version: Optional[str] = None,
    ) -> Optional["ArtifactVersionResponse"]:
        """Get the deployment artifact linked to this model version.

        Args:
            name: The name of the deployment artifact to retrieve.
            version: The version of the deployment artifact to retrieve (None for
                latest/non-versioned)

        Returns:
            Specific version of the deployment artifact or None
        """
        return self._get_linked_object(name, version, ArtifactType.SERVICE)

    def get_pipeline_run(self, name: str) -> "PipelineRunResponse":
        """Get pipeline run linked to this version.

        Args:
            name: The name of the pipeline run to retrieve.

        Returns:
            PipelineRun as PipelineRunResponseModel
        """
        from zenml.client import Client

        return Client().get_pipeline_run(self.pipeline_run_ids[name])

    def set_stage(
        self, stage: Union[str, ModelStages], force: bool = False
    ) -> None:
        """Sets this Model Version to a desired stage.

        Args:
            stage: the target stage for model version.
            force: whether to force archiving of current model version in
                target stage or raise.

        Raises:
            ValueError: if model_stage is not valid.
        """
        from zenml.client import Client

        stage = getattr(stage, "value", stage)
        if stage not in [stage.value for stage in ModelStages]:
            raise ValueError(f"`{stage}` is not a valid model stage.")

        Client().update_model_version(
            model_name_or_id=self.model.id,
            version_name_or_id=self.id,
            stage=stage,
            force=force,
        )

`data_artifact_ids` `property`

The data_artifact_ids property.

Returns:

Type	Description
`Dict[str, Dict[str, UUID]]`	the value of the property.

`data_artifacts` `property`

Get all data artifacts linked to this model version.

Returns:

Type	Description
`Dict[str, Dict[str, ArtifactVersionResponse]]`	Dictionary of data artifacts with versions as
`Dict[str, Dict[str, ArtifactVersionResponse]]`	Dict[str, Dict[str, ArtifactResponse]]

`deployment_artifact_ids` `property`

The deployment_artifact_ids property.

Returns:

Type	Description
`Dict[str, Dict[str, UUID]]`	the value of the property.

`deployment_artifacts` `property`

Get all deployment artifacts linked to this model version.

Returns:

Type	Description
`Dict[str, Dict[str, ArtifactVersionResponse]]`	Dictionary of deployment artifacts with versions as
`Dict[str, Dict[str, ArtifactVersionResponse]]`	Dict[str, Dict[str, ArtifactResponse]]

`description` `property`

The description property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`model` `property`

The model property.

Returns:

Type	Description
`ModelResponse`	the value of the property.

`model_artifact_ids` `property`

The model_artifact_ids property.

Returns:

Type	Description
`Dict[str, Dict[str, UUID]]`	the value of the property.

`model_artifacts` `property`

Get all model artifacts linked to this model version.

Returns:

Type	Description
`Dict[str, Dict[str, ArtifactVersionResponse]]`	Dictionary of model artifacts with versions as
`Dict[str, Dict[str, ArtifactVersionResponse]]`	Dict[str, Dict[str, ArtifactResponse]]

`number` `property`

The number property.

Returns:

Type	Description
`int`	the value of the property.

`pipeline_run_ids` `property`

The pipeline_run_ids property.

Returns:

Type	Description
`Dict[str, UUID]`	the value of the property.

`pipeline_runs` `property`

Get all pipeline runs linked to this version.

Returns:

Type	Description
`Dict[str, PipelineRunResponse]`	Dictionary of Pipeline Runs as PipelineRunResponseModel

`run_metadata` `property`

The run_metadata property.

Returns:

Type	Description
`Dict[str, MetadataType]`	the value of the property.

`stage` `property`

The stage property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`tags` `property`

The tags property.

Returns:

Type	Description
`List[TagResponse]`	the value of the property.

`get_artifact(name, version=None)`

Get the artifact linked to this model version.

Parameters:

Name	Type	Description	Default
`name`	`str`	The name of the artifact to retrieve.	required
`version`	`Optional[str]`	The version of the artifact to retrieve (None for latest/non-versioned)	`None`

Returns:

Type	Description
`Optional[ArtifactVersionResponse]`	Specific version of an artifact or None

Source code in src/zenml/models/v2/core/model_version.py

def get_artifact(
    self,
    name: str,
    version: Optional[str] = None,
) -> Optional["ArtifactVersionResponse"]:
    """Get the artifact linked to this model version.

    Args:
        name: The name of the artifact to retrieve.
        version: The version of the artifact to retrieve (None for
            latest/non-versioned)

    Returns:
        Specific version of an artifact or None
    """
    return self._get_linked_object(name, version)

`get_data_artifact(name, version=None)`

Get the data artifact linked to this model version.

Parameters:

Name	Type	Description	Default
`name`	`str`	The name of the data artifact to retrieve.	required
`version`	`Optional[str]`	The version of the data artifact to retrieve (None for latest/non-versioned)	`None`

Returns:

Type	Description
`Optional[ArtifactVersionResponse]`	Specific version of the data artifact or None

Source code in src/zenml/models/v2/core/model_version.py

def get_data_artifact(
    self,
    name: str,
    version: Optional[str] = None,
) -> Optional["ArtifactVersionResponse"]:
    """Get the data artifact linked to this model version.

    Args:
        name: The name of the data artifact to retrieve.
        version: The version of the data artifact to retrieve (None for
            latest/non-versioned)

    Returns:
        Specific version of the data artifact or None
    """
    return self._get_linked_object(name, version, ArtifactType.DATA)

`get_deployment_artifact(name, version=None)`

Get the deployment artifact linked to this model version.

Parameters:

Name	Type	Description	Default
`name`	`str`	The name of the deployment artifact to retrieve.	required
`version`	`Optional[str]`	The version of the deployment artifact to retrieve (None for latest/non-versioned)	`None`

Returns:

Type	Description
`Optional[ArtifactVersionResponse]`	Specific version of the deployment artifact or None

Source code in src/zenml/models/v2/core/model_version.py

def get_deployment_artifact(
    self,
    name: str,
    version: Optional[str] = None,
) -> Optional["ArtifactVersionResponse"]:
    """Get the deployment artifact linked to this model version.

    Args:
        name: The name of the deployment artifact to retrieve.
        version: The version of the deployment artifact to retrieve (None for
            latest/non-versioned)

    Returns:
        Specific version of the deployment artifact or None
    """
    return self._get_linked_object(name, version, ArtifactType.SERVICE)

`get_hydrated_version()`

Get the hydrated version of this model version.

Returns:

Type	Description
`ModelVersionResponse`	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/model_version.py

def get_hydrated_version(self) -> "ModelVersionResponse":
    """Get the hydrated version of this model version.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_model_version(self.id)

`get_model_artifact(name, version=None)`

Get the model artifact linked to this model version.

Parameters:

Name	Type	Description	Default
`name`	`str`	The name of the model artifact to retrieve.	required
`version`	`Optional[str]`	The version of the model artifact to retrieve (None for latest/non-versioned)	`None`

Returns:

Type	Description
`Optional[ArtifactVersionResponse]`	Specific version of the model artifact or None

Source code in src/zenml/models/v2/core/model_version.py

def get_model_artifact(
    self,
    name: str,
    version: Optional[str] = None,
) -> Optional["ArtifactVersionResponse"]:
    """Get the model artifact linked to this model version.

    Args:
        name: The name of the model artifact to retrieve.
        version: The version of the model artifact to retrieve (None for
            latest/non-versioned)

    Returns:
        Specific version of the model artifact or None
    """
    return self._get_linked_object(name, version, ArtifactType.MODEL)

`get_pipeline_run(name)`

Get pipeline run linked to this version.

Parameters:

Name	Type	Description	Default
`name`	`str`	The name of the pipeline run to retrieve.	required

Returns:

Type	Description
`PipelineRunResponse`	PipelineRun as PipelineRunResponseModel

Source code in src/zenml/models/v2/core/model_version.py

def get_pipeline_run(self, name: str) -> "PipelineRunResponse":
    """Get pipeline run linked to this version.

    Args:
        name: The name of the pipeline run to retrieve.

    Returns:
        PipelineRun as PipelineRunResponseModel
    """
    from zenml.client import Client

    return Client().get_pipeline_run(self.pipeline_run_ids[name])

`set_stage(stage, force=False)`

Sets this Model Version to a desired stage.

Parameters:

Name	Type	Description	Default
`stage`	`Union[str, ModelStages]`	the target stage for model version.	required
`force`	`bool`	whether to force archiving of current model version in target stage or raise.	`False`

Raises:

Type	Description
`ValueError`	if model_stage is not valid.

Source code in src/zenml/models/v2/core/model_version.py

def set_stage(
    self, stage: Union[str, ModelStages], force: bool = False
) -> None:
    """Sets this Model Version to a desired stage.

    Args:
        stage: the target stage for model version.
        force: whether to force archiving of current model version in
            target stage or raise.

    Raises:
        ValueError: if model_stage is not valid.
    """
    from zenml.client import Client

    stage = getattr(stage, "value", stage)
    if stage not in [stage.value for stage in ModelStages]:
        raise ValueError(f"`{stage}` is not a valid model stage.")

    Client().update_model_version(
        model_name_or_id=self.model.id,
        version_name_or_id=self.id,
        stage=stage,
        force=force,
    )

`to_model_class(suppress_class_validation_warnings=True)`

Convert response model to Model object.

Parameters:

Name	Type	Description	Default
`suppress_class_validation_warnings`	`bool`	internally used to suppress repeated warnings.	`True`

Returns:

Type	Description
`Model`	Model object

Source code in src/zenml/models/v2/core/model_version.py

def to_model_class(
    self,
    suppress_class_validation_warnings: bool = True,
) -> "Model":
    """Convert response model to Model object.

    Args:
        suppress_class_validation_warnings: internally used to suppress
            repeated warnings.

    Returns:
        Model object
    """
    from zenml.model.model import Model

    mv = Model(
        name=self.model.name,
        license=self.model.license,
        description=self.description,
        audience=self.model.audience,
        use_cases=self.model.use_cases,
        limitations=self.model.limitations,
        trade_offs=self.model.trade_offs,
        ethics=self.model.ethics,
        tags=[t.name for t in self.tags],
        version=self.name,
        suppress_class_validation_warnings=suppress_class_validation_warnings,
        model_version_id=self.id,
    )

    return mv

`ModelVersionResponseBody`

Response body for model versions.

Source code in src/zenml/models/v2/core/model_version.py

class ModelVersionResponseBody(WorkspaceScopedResponseBody):
    """Response body for model versions."""

    stage: Optional[str] = Field(
        description="The stage of the model version",
        max_length=STR_FIELD_MAX_LENGTH,
        default=None,
    )
    number: int = Field(
        description="The number of the model version",
    )
    model: "ModelResponse" = Field(
        description="The model containing version",
    )
    model_artifact_ids: Dict[str, Dict[str, UUID]] = Field(
        description="Model artifacts linked to the model version",
        default={},
    )
    data_artifact_ids: Dict[str, Dict[str, UUID]] = Field(
        description="Data artifacts linked to the model version",
        default={},
    )
    deployment_artifact_ids: Dict[str, Dict[str, UUID]] = Field(
        description="Deployment artifacts linked to the model version",
        default={},
    )
    pipeline_run_ids: Dict[str, UUID] = Field(
        description="Pipeline runs linked to the model version",
        default={},
    )
    tags: List[TagResponse] = Field(
        title="Tags associated with the model version", default=[]
    )

    # TODO: In Pydantic v2, the `model_` is a protected namespaces for all
    #  fields defined under base models. If not handled, this raises a warning.
    #  It is possible to suppress this warning message with the following
    #  configuration, however the ultimate solution is to rename these fields.
    #  Even though they do not cause any problems right now, if we are not
    #  careful we might overwrite some fields protected by pydantic.
    model_config = ConfigDict(protected_namespaces=())

`ModelVersionResponseMetadata`

Response metadata for model versions.

Source code in src/zenml/models/v2/core/model_version.py

class ModelVersionResponseMetadata(WorkspaceScopedResponseMetadata):
    """Response metadata for model versions."""

    description: Optional[str] = Field(
        description="The description of the model version",
        max_length=TEXT_FIELD_MAX_LENGTH,
        default=None,
    )
    run_metadata: Dict[str, MetadataType] = Field(
        description="Metadata linked to the model version",
        default={},
    )

`ModelVersionResponseResources`

Class for all resource models associated with the model version entity.

Source code in src/zenml/models/v2/core/model_version.py

class ModelVersionResponseResources(WorkspaceScopedResponseResources):
    """Class for all resource models associated with the model version entity."""

    services: Page[ServiceResponse] = Field(
        description="Services linked to the model version",
    )

`ModelVersionUpdate`

Bases: BaseModel

Update model for model versions.

Source code in src/zenml/models/v2/core/model_version.py

class ModelVersionUpdate(BaseModel):
    """Update model for model versions."""

    model: UUID = Field(
        description="The ID of the model containing version",
    )
    stage: Optional[Union[str, ModelStages]] = Field(
        description="Target model version stage to be set",
        default=None,
        union_mode="left_to_right",
    )
    force: bool = Field(
        description="Whether existing model version in target stage should be "
        "silently archived or an error should be raised.",
        default=False,
    )
    name: Optional[str] = Field(
        description="Target model version name to be set",
        default=None,
    )
    description: Optional[str] = Field(
        description="Target model version description to be set",
        default=None,
    )
    add_tags: Optional[List[str]] = Field(
        description="Tags to be added to the model version",
        default=None,
    )
    remove_tags: Optional[List[str]] = Field(
        description="Tags to be removed from the model version",
        default=None,
    )

    @field_validator("stage")
    @classmethod
    def _validate_stage(cls, stage: str) -> str:
        stage = getattr(stage, "value", stage)
        if stage is not None and stage not in [
            stage.value for stage in ModelStages
        ]:
            raise ValueError(f"`{stage}` is not a valid model stage.")
        return stage

`NumericFilter`

Bases: Filter

Filter for all numeric fields.

Source code in src/zenml/models/v2/base/filter.py

class NumericFilter(Filter):
    """Filter for all numeric fields."""

    value: Union[float, datetime] = Field(union_mode="left_to_right")

    ALLOWED_OPS: ClassVar[List[str]] = [
        GenericFilterOps.EQUALS,
        GenericFilterOps.NOT_EQUALS,
        GenericFilterOps.GT,
        GenericFilterOps.GTE,
        GenericFilterOps.LT,
        GenericFilterOps.LTE,
    ]

    def generate_query_conditions_from_column(self, column: Any) -> Any:
        """Generate query conditions for a numeric column.

        Args:
            column: The numeric column of an SQLModel table on which to filter.

        Returns:
            A list of query conditions.
        """
        if self.operation == GenericFilterOps.GTE:
            return column >= self.value
        if self.operation == GenericFilterOps.GT:
            return column > self.value
        if self.operation == GenericFilterOps.LTE:
            return column <= self.value
        if self.operation == GenericFilterOps.LT:
            return column < self.value
        if self.operation == GenericFilterOps.NOT_EQUALS:
            return column != self.value
        return column == self.value

`generate_query_conditions_from_column(column)`

Generate query conditions for a numeric column.

Parameters:

Name	Type	Description	Default
`column`	`Any`	The numeric column of an SQLModel table on which to filter.	required

Returns:

Type	Description
`Any`	A list of query conditions.

Source code in src/zenml/models/v2/base/filter.py

def generate_query_conditions_from_column(self, column: Any) -> Any:
    """Generate query conditions for a numeric column.

    Args:
        column: The numeric column of an SQLModel table on which to filter.

    Returns:
        A list of query conditions.
    """
    if self.operation == GenericFilterOps.GTE:
        return column >= self.value
    if self.operation == GenericFilterOps.GT:
        return column > self.value
    if self.operation == GenericFilterOps.LTE:
        return column <= self.value
    if self.operation == GenericFilterOps.LT:
        return column < self.value
    if self.operation == GenericFilterOps.NOT_EQUALS:
        return column != self.value
    return column == self.value

`OAuthDeviceAuthorizationRequest`

Bases: BaseModel

OAuth2 device authorization grant request.

Source code in src/zenml/models/v2/misc/auth_models.py

class OAuthDeviceAuthorizationRequest(BaseModel):
    """OAuth2 device authorization grant request."""

    client_id: UUID
    device_id: Optional[UUID] = None

`OAuthDeviceAuthorizationResponse`

Bases: BaseModel

OAuth2 device authorization grant response.

Source code in src/zenml/models/v2/misc/auth_models.py

class OAuthDeviceAuthorizationResponse(BaseModel):
    """OAuth2 device authorization grant response."""

    device_code: str
    user_code: str
    verification_uri: str
    verification_uri_complete: Optional[str] = None
    expires_in: int
    interval: int

`OAuthDeviceFilter`

Bases: UserScopedFilter

Model to enable advanced filtering of OAuth2 devices.

Source code in src/zenml/models/v2/core/device.py

class OAuthDeviceFilter(UserScopedFilter):
    """Model to enable advanced filtering of OAuth2 devices."""

    expires: Optional[Union[datetime, str, None]] = Field(
        default=None,
        description="The expiration date of the OAuth2 device.",
        union_mode="left_to_right",
    )
    client_id: Union[UUID, str, None] = Field(
        default=None,
        description="The client ID of the OAuth2 device.",
        union_mode="left_to_right",
    )
    status: Union[OAuthDeviceStatus, str, None] = Field(
        default=None,
        description="The status of the OAuth2 device.",
        union_mode="left_to_right",
    )
    trusted_device: Union[bool, str, None] = Field(
        default=None,
        description="Whether the OAuth2 device was marked as trusted.",
        union_mode="left_to_right",
    )
    failed_auth_attempts: Union[int, str, None] = Field(
        default=None,
        description="The number of failed authentication attempts.",
        union_mode="left_to_right",
    )
    last_login: Optional[Union[datetime, str, None]] = Field(
        default=None,
        description="The date of the last successful login.",
        union_mode="left_to_right",
    )

`OAuthDeviceInternalRequest`

Bases: BaseRequest

Internal request model for OAuth2 devices.

Source code in src/zenml/models/v2/core/device.py

class OAuthDeviceInternalRequest(BaseRequest):
    """Internal request model for OAuth2 devices."""

    client_id: UUID = Field(description="The client ID of the OAuth2 device.")
    expires_in: int = Field(
        description="The number of seconds after which the OAuth2 device "
        "expires and can no longer be used for authentication."
    )
    os: Optional[str] = Field(
        default=None,
        description="The operating system of the device used for "
        "authentication.",
    )
    ip_address: Optional[str] = Field(
        default=None,
        description="The IP address of the device used for authentication.",
    )
    hostname: Optional[str] = Field(
        default=None,
        description="The hostname of the device used for authentication.",
    )
    python_version: Optional[str] = Field(
        default=None,
        description="The Python version of the device used for authentication.",
    )
    zenml_version: Optional[str] = Field(
        default=None,
        description="The ZenML version of the device used for authentication.",
    )
    city: Optional[str] = Field(
        default=None,
        description="The city where the device is located.",
    )
    region: Optional[str] = Field(
        default=None,
        description="The region where the device is located.",
    )
    country: Optional[str] = Field(
        default=None,
        description="The country where the device is located.",
    )

`OAuthDeviceInternalResponse`

Bases: OAuthDeviceResponse

OAuth2 device response model used internally for authentication.

Source code in src/zenml/models/v2/core/device.py

class OAuthDeviceInternalResponse(OAuthDeviceResponse):
    """OAuth2 device response model used internally for authentication."""

    user_code: str = Field(
        title="The user code.",
    )
    device_code: str = Field(
        title="The device code.",
    )

    def _verify_code(
        self,
        code: str,
        code_hash: Optional[str],
    ) -> bool:
        """Verifies a given code against the stored (hashed) code.

        Args:
            code: The code to verify.
            code_hash: The hashed code to verify against.

        Returns:
            True if the code is valid, False otherwise.
        """
        context = CryptContext(schemes=["bcrypt"], deprecated="auto")
        result = context.verify(code, code_hash)

        return result

    def verify_user_code(
        self,
        user_code: str,
    ) -> bool:
        """Verifies a given user code against the stored (hashed) user code.

        Args:
            user_code: The user code to verify.

        Returns:
            True if the user code is valid, False otherwise.
        """
        return self._verify_code(user_code, self.user_code)

    def verify_device_code(
        self,
        device_code: str,
    ) -> bool:
        """Verifies a given device code against the stored (hashed) device code.

        Args:
            device_code: The device code to verify.

        Returns:
            True if the device code is valid, False otherwise.
        """
        return self._verify_code(device_code, self.device_code)

`verify_device_code(device_code)`

Verifies a given device code against the stored (hashed) device code.

Parameters:

Name	Type	Description	Default
`device_code`	`str`	The device code to verify.	required

Returns:

Type	Description
`bool`	True if the device code is valid, False otherwise.

Source code in src/zenml/models/v2/core/device.py

def verify_device_code(
    self,
    device_code: str,
) -> bool:
    """Verifies a given device code against the stored (hashed) device code.

    Args:
        device_code: The device code to verify.

    Returns:
        True if the device code is valid, False otherwise.
    """
    return self._verify_code(device_code, self.device_code)

`verify_user_code(user_code)`

Verifies a given user code against the stored (hashed) user code.

Parameters:

Name	Type	Description	Default
`user_code`	`str`	The user code to verify.	required

Returns:

Type	Description
`bool`	True if the user code is valid, False otherwise.

Source code in src/zenml/models/v2/core/device.py

def verify_user_code(
    self,
    user_code: str,
) -> bool:
    """Verifies a given user code against the stored (hashed) user code.

    Args:
        user_code: The user code to verify.

    Returns:
        True if the user code is valid, False otherwise.
    """
    return self._verify_code(user_code, self.user_code)

`OAuthDeviceInternalUpdate`

Bases: OAuthDeviceUpdate

OAuth2 device update model used internally for authentication.

Source code in src/zenml/models/v2/core/device.py

class OAuthDeviceInternalUpdate(OAuthDeviceUpdate):
    """OAuth2 device update model used internally for authentication."""

    user_id: Optional[UUID] = Field(
        default=None, description="User that owns the OAuth2 device."
    )
    status: Optional[OAuthDeviceStatus] = Field(
        default=None, description="The new status of the OAuth2 device."
    )
    expires_in: Optional[int] = Field(
        default=None,
        description="Set the device to expire in the given number of seconds. "
        "If the value is 0 or negative, the device is set to never expire.",
    )
    failed_auth_attempts: Optional[int] = Field(
        default=None,
        description="Set the number of failed authentication attempts.",
    )
    trusted_device: Optional[bool] = Field(
        default=None,
        description="Whether to mark the OAuth2 device as trusted. A trusted "
        "device has a much longer validity time.",
    )
    update_last_login: bool = Field(
        default=False, description="Whether to update the last login date."
    )
    generate_new_codes: bool = Field(
        default=False,
        description="Whether to generate new user and device codes.",
    )
    os: Optional[str] = Field(
        default=None,
        description="The operating system of the device used for "
        "authentication.",
    )
    ip_address: Optional[str] = Field(
        default=None,
        description="The IP address of the device used for authentication.",
    )
    hostname: Optional[str] = Field(
        default=None,
        description="The hostname of the device used for authentication.",
    )
    python_version: Optional[str] = Field(
        default=None,
        description="The Python version of the device used for authentication.",
    )
    zenml_version: Optional[str] = Field(
        default=None,
        description="The ZenML version of the device used for authentication.",
    )
    city: Optional[str] = Field(
        default=None,
        description="The city where the device is located.",
    )
    region: Optional[str] = Field(
        default=None,
        description="The region where the device is located.",
    )
    country: Optional[str] = Field(
        default=None,
        description="The country where the device is located.",
    )

`OAuthDeviceResponse`

Bases: UserScopedResponse[OAuthDeviceResponseBody, OAuthDeviceResponseMetadata, OAuthDeviceResponseResources]

Response model for OAuth2 devices.

Source code in src/zenml/models/v2/core/device.py

class OAuthDeviceResponse(
    UserScopedResponse[
        OAuthDeviceResponseBody,
        OAuthDeviceResponseMetadata,
        OAuthDeviceResponseResources,
    ]
):
    """Response model for OAuth2 devices."""

    _warn_on_response_updates = False

    def get_hydrated_version(self) -> "OAuthDeviceResponse":
        """Get the hydrated version of this OAuth2 device.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_authorized_device(self.id)

    # Body and metadata properties
    @property
    def client_id(self) -> UUID:
        """The `client_id` property.

        Returns:
            the value of the property.
        """
        return self.get_body().client_id

    @property
    def expires(self) -> Optional[datetime]:
        """The `expires` property.

        Returns:
            the value of the property.
        """
        return self.get_body().expires

    @property
    def trusted_device(self) -> bool:
        """The `trusted_device` property.

        Returns:
            the value of the property.
        """
        return self.get_body().trusted_device

    @property
    def status(self) -> OAuthDeviceStatus:
        """The `status` property.

        Returns:
            the value of the property.
        """
        return self.get_body().status

    @property
    def os(self) -> Optional[str]:
        """The `os` property.

        Returns:
            the value of the property.
        """
        return self.get_body().os

    @property
    def ip_address(self) -> Optional[str]:
        """The `ip_address` property.

        Returns:
            the value of the property.
        """
        return self.get_body().ip_address

    @property
    def hostname(self) -> Optional[str]:
        """The `hostname` property.

        Returns:
            the value of the property.
        """
        return self.get_body().hostname

    @property
    def python_version(self) -> Optional[str]:
        """The `python_version` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().python_version

    @property
    def zenml_version(self) -> Optional[str]:
        """The `zenml_version` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().zenml_version

    @property
    def city(self) -> Optional[str]:
        """The `city` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().city

    @property
    def region(self) -> Optional[str]:
        """The `region` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().region

    @property
    def country(self) -> Optional[str]:
        """The `country` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().country

    @property
    def failed_auth_attempts(self) -> int:
        """The `failed_auth_attempts` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().failed_auth_attempts

    @property
    def last_login(self) -> Optional[datetime]:
        """The `last_login` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().last_login

`city` `property`

The city property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`client_id` `property`

The client_id property.

Returns:

Type	Description
`UUID`	the value of the property.

`country` `property`

The country property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`expires` `property`

The expires property.

Returns:

Type	Description
`Optional[datetime]`	the value of the property.

`failed_auth_attempts` `property`

The failed_auth_attempts property.

Returns:

Type	Description
`int`	the value of the property.

`hostname` `property`

The hostname property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`ip_address` `property`

The ip_address property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`last_login` `property`

The last_login property.

Returns:

Type	Description
`Optional[datetime]`	the value of the property.

`os` `property`

The os property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`python_version` `property`

The python_version property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`region` `property`

The region property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`status` `property`

The status property.

Returns:

Type	Description
`OAuthDeviceStatus`	the value of the property.

`trusted_device` `property`

The trusted_device property.

Returns:

Type	Description
`bool`	the value of the property.

`zenml_version` `property`

The zenml_version property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`get_hydrated_version()`

Get the hydrated version of this OAuth2 device.

Returns:

Type	Description
`OAuthDeviceResponse`	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/device.py

def get_hydrated_version(self) -> "OAuthDeviceResponse":
    """Get the hydrated version of this OAuth2 device.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_authorized_device(self.id)

`OAuthDeviceResponseBody`

Bases: UserScopedResponseBody

Response body for OAuth2 devices.

Source code in src/zenml/models/v2/core/device.py

class OAuthDeviceResponseBody(UserScopedResponseBody):
    """Response body for OAuth2 devices."""

    client_id: UUID = Field(description="The client ID of the OAuth2 device.")
    expires: Optional[datetime] = Field(
        default=None,
        description="The expiration date of the OAuth2 device after which "
        "the device is no longer valid and cannot be used for "
        "authentication.",
    )
    trusted_device: bool = Field(
        description="Whether the OAuth2 device was marked as trusted. A "
        "trusted device has a much longer validity time.",
    )
    status: OAuthDeviceStatus = Field(
        description="The status of the OAuth2 device."
    )
    os: Optional[str] = Field(
        default=None,
        description="The operating system of the device used for "
        "authentication.",
    )
    ip_address: Optional[str] = Field(
        default=None,
        description="The IP address of the device used for authentication.",
    )
    hostname: Optional[str] = Field(
        default=None,
        description="The hostname of the device used for authentication.",
    )

`OAuthDeviceResponseMetadata`

Bases: UserScopedResponseMetadata

Response metadata for OAuth2 devices.

Source code in src/zenml/models/v2/core/device.py

class OAuthDeviceResponseMetadata(UserScopedResponseMetadata):
    """Response metadata for OAuth2 devices."""

    python_version: Optional[str] = Field(
        default=None,
        description="The Python version of the device used for authentication.",
    )
    zenml_version: Optional[str] = Field(
        default=None,
        description="The ZenML version of the device used for authentication.",
    )
    city: Optional[str] = Field(
        default=None,
        description="The city where the device is located.",
    )
    region: Optional[str] = Field(
        default=None,
        description="The region where the device is located.",
    )
    country: Optional[str] = Field(
        default=None,
        description="The country where the device is located.",
    )
    failed_auth_attempts: int = Field(
        description="The number of failed authentication attempts.",
    )
    last_login: Optional[datetime] = Field(
        description="The date of the last successful login."
    )

`OAuthDeviceTokenRequest`

Bases: BaseModel

OAuth2 device authorization grant request.

Source code in src/zenml/models/v2/misc/auth_models.py

class OAuthDeviceTokenRequest(BaseModel):
    """OAuth2 device authorization grant request."""

    grant_type: str = OAuthGrantTypes.OAUTH_DEVICE_CODE
    client_id: UUID
    device_code: str

`OAuthDeviceUpdate`

Bases: BaseModel

OAuth2 device update model.

Source code in src/zenml/models/v2/core/device.py

class OAuthDeviceUpdate(BaseModel):
    """OAuth2 device update model."""

    locked: Optional[bool] = Field(
        default=None,
        description="Whether to lock or unlock the OAuth2 device. A locked "
        "device cannot be used for authentication.",
    )

`OAuthDeviceUserAgentHeader`

Bases: BaseModel

OAuth2 device user agent header.

Source code in src/zenml/models/v2/misc/auth_models.py

class OAuthDeviceUserAgentHeader(BaseModel):
    """OAuth2 device user agent header."""

    hostname: Optional[str] = None
    os: Optional[str] = None
    python_version: Optional[str] = None
    zenml_version: Optional[str] = None

    @classmethod
    def decode(cls, header_str: str) -> "OAuthDeviceUserAgentHeader":
        """Decode the user agent header.

        Args:
            header_str: The user agent header string value.

        Returns:
            The decoded user agent header.
        """
        header = cls()
        properties = header_str.strip().split(" ")
        for property in properties:
            try:
                key, value = property.split("/", maxsplit=1)
            except ValueError:
                continue
            if key == "Host":
                header.hostname = value
            elif key == "ZenML":
                header.zenml_version = value
            elif key == "Python":
                header.python_version = value
            elif key == "OS":
                header.os = value
        return header

    def encode(self) -> str:
        """Encode the user agent header.

        Returns:
            The encoded user agent header.
        """
        return (
            f"Host/{self.hostname} "
            f"ZenML/{self.zenml_version} "
            f"Python/{self.python_version} "
            f"OS/{self.os}"
        )

`decode(header_str)` `classmethod`

Decode the user agent header.

Parameters:

Name	Type	Description	Default
`header_str`	`str`	The user agent header string value.	required

Returns:

Type	Description
`OAuthDeviceUserAgentHeader`	The decoded user agent header.

Source code in src/zenml/models/v2/misc/auth_models.py

@classmethod
def decode(cls, header_str: str) -> "OAuthDeviceUserAgentHeader":
    """Decode the user agent header.

    Args:
        header_str: The user agent header string value.

    Returns:
        The decoded user agent header.
    """
    header = cls()
    properties = header_str.strip().split(" ")
    for property in properties:
        try:
            key, value = property.split("/", maxsplit=1)
        except ValueError:
            continue
        if key == "Host":
            header.hostname = value
        elif key == "ZenML":
            header.zenml_version = value
        elif key == "Python":
            header.python_version = value
        elif key == "OS":
            header.os = value
    return header

`encode()`

Encode the user agent header.

Returns:

Type	Description
`str`	The encoded user agent header.

Source code in src/zenml/models/v2/misc/auth_models.py

def encode(self) -> str:
    """Encode the user agent header.

    Returns:
        The encoded user agent header.
    """
    return (
        f"Host/{self.hostname} "
        f"ZenML/{self.zenml_version} "
        f"Python/{self.python_version} "
        f"OS/{self.os}"
    )

`OAuthDeviceVerificationRequest`

Bases: BaseModel

OAuth2 device authorization verification request.

Source code in src/zenml/models/v2/misc/auth_models.py

class OAuthDeviceVerificationRequest(BaseModel):
    """OAuth2 device authorization verification request."""

    user_code: str
    trusted_device: bool = False

`OAuthRedirectResponse`

Bases: BaseModel

Redirect response.

Source code in src/zenml/models/v2/misc/auth_models.py

class OAuthRedirectResponse(BaseModel):
    """Redirect response."""

    authorization_url: str

`OAuthTokenResponse`

Bases: BaseModel

OAuth2 device authorization token response.

Source code in src/zenml/models/v2/misc/auth_models.py

class OAuthTokenResponse(BaseModel):
    """OAuth2 device authorization token response."""

    access_token: str
    token_type: str
    expires_in: Optional[int] = None
    refresh_token: Optional[str] = None
    csrf_token: Optional[str] = None
    scope: Optional[str] = None
    device_id: Optional[UUID] = None
    device_metadata: Optional[Dict[str, Any]] = None

    model_config = ConfigDict(
        # Allow extra attributes to allow compatibility with different versions
        extra="allow",
    )

`Page`

Bases: BaseModel, Generic[B]

Return Model for List Models to accommodate pagination.

Source code in src/zenml/models/v2/base/page.py

class Page(BaseModel, Generic[B]):
    """Return Model for List Models to accommodate pagination."""

    index: PositiveInt
    max_size: PositiveInt
    total_pages: NonNegativeInt
    total: NonNegativeInt
    items: List[B]

    __params_type__ = BaseFilter

    @property
    def size(self) -> int:
        """Return the item count of the page.

        Returns:
            The amount of items in the page.
        """
        return len(self.items)

    def __len__(self) -> int:
        """Return the item count of the page.

        This enables `len(page)`.

        Returns:
            The amount of items in the page.
        """
        return len(self.items)

    def __getitem__(self, index: int) -> B:
        """Return the item at the given index.

        This enables `page[index]`.

        Args:
            index: The index to get the item from.

        Returns:
            The item at the given index.
        """
        return self.items[index]

    def __iter__(self) -> Generator[B, None, None]:  # type: ignore[override]
        """Return an iterator over the items in the page.

        This enables `for item in page` loops, but breaks `dict(page)`.

        Yields:
            An iterator over the items in the page.
        """
        for item in self.items.__iter__():
            yield item

    def __contains__(self, item: B) -> bool:
        """Returns whether the page contains a specific item.

        This enables `item in page` checks.

        Args:
            item: The item to check for.

        Returns:
            Whether the item is in the page.
        """
        return item in self.items

`size` `property`

Return the item count of the page.

Returns:

Type	Description
`int`	The amount of items in the page.

`contains(item)`

Returns whether the page contains a specific item.

This enables item in page checks.

Parameters:

Name	Type	Description	Default
`item`	`B`	The item to check for.	required

Returns:

Type	Description
`bool`	Whether the item is in the page.

Source code in src/zenml/models/v2/base/page.py

def __contains__(self, item: B) -> bool:
    """Returns whether the page contains a specific item.

    This enables `item in page` checks.

    Args:
        item: The item to check for.

    Returns:
        Whether the item is in the page.
    """
    return item in self.items

`getitem(index)`

Return the item at the given index.

This enables page[index].

Parameters:

Name	Type	Description	Default
`index`	`int`	The index to get the item from.	required

Returns:

Type	Description
`B`	The item at the given index.

Source code in src/zenml/models/v2/base/page.py

def __getitem__(self, index: int) -> B:
    """Return the item at the given index.

    This enables `page[index]`.

    Args:
        index: The index to get the item from.

    Returns:
        The item at the given index.
    """
    return self.items[index]

`iter()`

Return an iterator over the items in the page.

This enables for item in page loops, but breaks dict(page).

Yields:

Type	Description
`B`	An iterator over the items in the page.

Source code in src/zenml/models/v2/base/page.py

def __iter__(self) -> Generator[B, None, None]:  # type: ignore[override]
    """Return an iterator over the items in the page.

    This enables `for item in page` loops, but breaks `dict(page)`.

    Yields:
        An iterator over the items in the page.
    """
    for item in self.items.__iter__():
        yield item

`len()`

Return the item count of the page.

This enables len(page).

Returns:

Type	Description
`int`	The amount of items in the page.

Source code in src/zenml/models/v2/base/page.py

def __len__(self) -> int:
    """Return the item count of the page.

    This enables `len(page)`.

    Returns:
        The amount of items in the page.
    """
    return len(self.items)

`PipelineBuildBase`

Bases: BaseZenModel

Base model for pipeline builds.

Source code in src/zenml/models/v2/core/pipeline_build.py

class PipelineBuildBase(BaseZenModel):
    """Base model for pipeline builds."""

    images: Dict[str, BuildItem] = Field(
        default={}, title="The images of this build."
    )
    is_local: bool = Field(
        title="Whether the build images are stored in a container registry "
        "or locally.",
    )
    contains_code: bool = Field(
        title="Whether any image of the build contains user code.",
    )
    zenml_version: Optional[str] = Field(
        title="The version of ZenML used for this build.", default=None
    )
    python_version: Optional[str] = Field(
        title="The Python version used for this build.", default=None
    )

    # Helper methods
    @property
    def requires_code_download(self) -> bool:
        """Whether the build requires code download.

        Returns:
            Whether the build requires code download.
        """
        return any(
            item.requires_code_download for item in self.images.values()
        )

    @staticmethod
    def get_image_key(component_key: str, step: Optional[str] = None) -> str:
        """Get the image key.

        Args:
            component_key: The component key.
            step: The pipeline step for which the image was built.

        Returns:
            The image key.
        """
        if step:
            return f"{step}.{component_key}"
        else:
            return component_key

    def get_image(self, component_key: str, step: Optional[str] = None) -> str:
        """Get the image built for a specific key.

        Args:
            component_key: The key for which to get the image.
            step: The pipeline step for which to get the image. If no image
                exists for this step, will fall back to the pipeline image for
                the same key.

        Returns:
            The image name or digest.
        """
        return self._get_item(component_key=component_key, step=step).image

    def get_settings_checksum(
        self, component_key: str, step: Optional[str] = None
    ) -> Optional[str]:
        """Get the settings checksum for a specific key.

        Args:
            component_key: The key for which to get the checksum.
            step: The pipeline step for which to get the checksum. If no
                image exists for this step, will fall back to the pipeline image
                for the same key.

        Returns:
            The settings checksum.
        """
        return self._get_item(
            component_key=component_key, step=step
        ).settings_checksum

    def _get_item(
        self, component_key: str, step: Optional[str] = None
    ) -> "BuildItem":
        """Get the item for a specific key.

        Args:
            component_key: The key for which to get the item.
            step: The pipeline step for which to get the item. If no item
                exists for this step, will fall back to the item for
                the same key.

        Raises:
            KeyError: If no item exists for the given key.

        Returns:
            The build item.
        """
        if step:
            try:
                combined_key = self.get_image_key(
                    component_key=component_key, step=step
                )
                return self.images[combined_key]
            except KeyError:
                pass

        try:
            return self.images[component_key]
        except KeyError:
            raise KeyError(
                f"Unable to find image for key {component_key}. Available keys: "
                f"{set(self.images)}."
            )

`requires_code_download` `property`

Whether the build requires code download.

Returns:

Type	Description
`bool`	Whether the build requires code download.

`get_image(component_key, step=None)`

Get the image built for a specific key.

Parameters:

Name	Type	Description	Default
`component_key`	`str`	The key for which to get the image.	required
`step`	`Optional[str]`	The pipeline step for which to get the image. If no image exists for this step, will fall back to the pipeline image for the same key.	`None`

Returns:

Type	Description
`str`	The image name or digest.

Source code in src/zenml/models/v2/core/pipeline_build.py

def get_image(self, component_key: str, step: Optional[str] = None) -> str:
    """Get the image built for a specific key.

    Args:
        component_key: The key for which to get the image.
        step: The pipeline step for which to get the image. If no image
            exists for this step, will fall back to the pipeline image for
            the same key.

    Returns:
        The image name or digest.
    """
    return self._get_item(component_key=component_key, step=step).image

`get_image_key(component_key, step=None)` `staticmethod`

Get the image key.

Parameters:

Name	Type	Description	Default
`component_key`	`str`	The component key.	required
`step`	`Optional[str]`	The pipeline step for which the image was built.	`None`

Returns:

Type	Description
`str`	The image key.

Source code in src/zenml/models/v2/core/pipeline_build.py

@staticmethod
def get_image_key(component_key: str, step: Optional[str] = None) -> str:
    """Get the image key.

    Args:
        component_key: The component key.
        step: The pipeline step for which the image was built.

    Returns:
        The image key.
    """
    if step:
        return f"{step}.{component_key}"
    else:
        return component_key

`get_settings_checksum(component_key, step=None)`

Get the settings checksum for a specific key.

Parameters:

Name	Type	Description	Default
`component_key`	`str`	The key for which to get the checksum.	required
`step`	`Optional[str]`	The pipeline step for which to get the checksum. If no image exists for this step, will fall back to the pipeline image for the same key.	`None`

Returns:

Type	Description
`Optional[str]`	The settings checksum.

Source code in src/zenml/models/v2/core/pipeline_build.py

def get_settings_checksum(
    self, component_key: str, step: Optional[str] = None
) -> Optional[str]:
    """Get the settings checksum for a specific key.

    Args:
        component_key: The key for which to get the checksum.
        step: The pipeline step for which to get the checksum. If no
            image exists for this step, will fall back to the pipeline image
            for the same key.

    Returns:
        The settings checksum.
    """
    return self._get_item(
        component_key=component_key, step=step
    ).settings_checksum

`PipelineBuildFilter`

Model to enable advanced filtering of all pipeline builds.

Source code in src/zenml/models/v2/core/pipeline_build.py

class PipelineBuildFilter(WorkspaceScopedFilter):
    """Model to enable advanced filtering of all pipeline builds."""

    FILTER_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        *WorkspaceScopedFilter.FILTER_EXCLUDE_FIELDS,
        "container_registry_id",
    ]

    pipeline_id: Optional[Union[UUID, str]] = Field(
        description="Pipeline associated with the pipeline build.",
        default=None,
        union_mode="left_to_right",
    )
    stack_id: Optional[Union[UUID, str]] = Field(
        description="Stack associated with the pipeline build.",
        default=None,
        union_mode="left_to_right",
    )
    container_registry_id: Optional[Union[UUID, str]] = Field(
        description="Container registry associated with the pipeline build.",
        default=None,
        union_mode="left_to_right",
    )
    is_local: Optional[bool] = Field(
        description="Whether the build images are stored in a container "
        "registry or locally.",
        default=None,
    )
    contains_code: Optional[bool] = Field(
        description="Whether any image of the build contains user code.",
        default=None,
    )
    zenml_version: Optional[str] = Field(
        description="The version of ZenML used for this build.", default=None
    )
    python_version: Optional[str] = Field(
        description="The Python version used for this build.", default=None
    )
    checksum: Optional[str] = Field(
        description="The build checksum.", default=None
    )
    stack_checksum: Optional[str] = Field(
        description="The stack checksum.", default=None
    )

    def get_custom_filters(
        self,
        table: Type["AnySchema"],
    ) -> List["ColumnElement[bool]"]:
        """Get custom filters.

        Args:
            table: The query table.

        Returns:
            A list of custom filters.
        """
        custom_filters = super().get_custom_filters(table)

        from sqlmodel import and_

        from zenml.enums import StackComponentType
        from zenml.zen_stores.schemas import (
            PipelineBuildSchema,
            StackComponentSchema,
            StackCompositionSchema,
            StackSchema,
        )

        if self.container_registry_id:
            container_registry_filter = and_(
                PipelineBuildSchema.stack_id == StackSchema.id,
                StackSchema.id == StackCompositionSchema.stack_id,
                StackCompositionSchema.component_id == StackComponentSchema.id,
                StackComponentSchema.type
                == StackComponentType.CONTAINER_REGISTRY.value,
                StackComponentSchema.id == self.container_registry_id,
            )
            custom_filters.append(container_registry_filter)

        return custom_filters

`get_custom_filters(table)`

Get custom filters.

Parameters:

Name	Type	Description	Default
`table`	`Type[AnySchema]`	The query table.	required

Returns:

Type	Description
`List[ColumnElement[bool]]`	A list of custom filters.

Source code in src/zenml/models/v2/core/pipeline_build.py

def get_custom_filters(
    self,
    table: Type["AnySchema"],
) -> List["ColumnElement[bool]"]:
    """Get custom filters.

    Args:
        table: The query table.

    Returns:
        A list of custom filters.
    """
    custom_filters = super().get_custom_filters(table)

    from sqlmodel import and_

    from zenml.enums import StackComponentType
    from zenml.zen_stores.schemas import (
        PipelineBuildSchema,
        StackComponentSchema,
        StackCompositionSchema,
        StackSchema,
    )

    if self.container_registry_id:
        container_registry_filter = and_(
            PipelineBuildSchema.stack_id == StackSchema.id,
            StackSchema.id == StackCompositionSchema.stack_id,
            StackCompositionSchema.component_id == StackComponentSchema.id,
            StackComponentSchema.type
            == StackComponentType.CONTAINER_REGISTRY.value,
            StackComponentSchema.id == self.container_registry_id,
        )
        custom_filters.append(container_registry_filter)

    return custom_filters

`PipelineBuildRequest`

Bases: PipelineBuildBase, WorkspaceScopedRequest

Request model for pipelines builds.

Source code in src/zenml/models/v2/core/pipeline_build.py

class PipelineBuildRequest(PipelineBuildBase, WorkspaceScopedRequest):
    """Request model for pipelines builds."""

    checksum: Optional[str] = Field(title="The build checksum.", default=None)
    stack_checksum: Optional[str] = Field(
        title="The stack checksum.", default=None
    )

    stack: Optional[UUID] = Field(
        title="The stack that was used for this build.", default=None
    )
    pipeline: Optional[UUID] = Field(
        title="The pipeline that was used for this build.", default=None
    )

`PipelineBuildResponse`

Bases: WorkspaceScopedResponse[PipelineBuildResponseBody, PipelineBuildResponseMetadata, PipelineBuildResponseResources]

Response model for pipeline builds.

Source code in src/zenml/models/v2/core/pipeline_build.py

class PipelineBuildResponse(
    WorkspaceScopedResponse[
        PipelineBuildResponseBody,
        PipelineBuildResponseMetadata,
        PipelineBuildResponseResources,
    ]
):
    """Response model for pipeline builds."""

    def get_hydrated_version(self) -> "PipelineBuildResponse":
        """Return the hydrated version of this pipeline build.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_build(self.id)

    # Helper methods
    def to_yaml(self) -> Dict[str, Any]:
        """Create a yaml representation of the pipeline build.

        Create a yaml representation of the pipeline build that can be used
        to create a PipelineBuildBase instance.

        Returns:
            The yaml representation of the pipeline build.
        """
        # Get the base attributes
        yaml_dict: Dict[str, Any] = json.loads(
            self.model_dump_json(
                exclude={
                    "body",
                    "metadata",
                }
            )
        )
        images = json.loads(
            self.get_metadata().model_dump_json(
                exclude={
                    "pipeline",
                    "stack",
                    "workspace",
                }
            )
        )
        yaml_dict.update(images)
        return yaml_dict

    @property
    def requires_code_download(self) -> bool:
        """Whether the build requires code download.

        Returns:
            Whether the build requires code download.
        """
        return any(
            item.requires_code_download for item in self.images.values()
        )

    @staticmethod
    def get_image_key(component_key: str, step: Optional[str] = None) -> str:
        """Get the image key.

        Args:
            component_key: The component key.
            step: The pipeline step for which the image was built.

        Returns:
            The image key.
        """
        if step:
            return f"{step}.{component_key}"
        else:
            return component_key

    def get_image(self, component_key: str, step: Optional[str] = None) -> str:
        """Get the image built for a specific key.

        Args:
            component_key: The key for which to get the image.
            step: The pipeline step for which to get the image. If no image
                exists for this step, will fall back to the pipeline image for
                the same key.

        Returns:
            The image name or digest.
        """
        return self._get_item(component_key=component_key, step=step).image

    def get_settings_checksum(
        self, component_key: str, step: Optional[str] = None
    ) -> Optional[str]:
        """Get the settings checksum for a specific key.

        Args:
            component_key: The key for which to get the checksum.
            step: The pipeline step for which to get the checksum. If no
                image exists for this step, will fall back to the pipeline image
                for the same key.

        Returns:
            The settings checksum.
        """
        return self._get_item(
            component_key=component_key, step=step
        ).settings_checksum

    def _get_item(
        self, component_key: str, step: Optional[str] = None
    ) -> "BuildItem":
        """Get the item for a specific key.

        Args:
            component_key: The key for which to get the item.
            step: The pipeline step for which to get the item. If no item
                exists for this step, will fall back to the item for
                the same key.

        Raises:
            KeyError: If no item exists for the given key.

        Returns:
            The build item.
        """
        if step:
            try:
                combined_key = self.get_image_key(
                    component_key=component_key, step=step
                )
                return self.images[combined_key]
            except KeyError:
                pass

        try:
            return self.images[component_key]
        except KeyError:
            raise KeyError(
                f"Unable to find image for key {component_key}. Available keys: "
                f"{set(self.images)}."
            )

    # Body and metadata properties
    @property
    def pipeline(self) -> Optional["PipelineResponse"]:
        """The `pipeline` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().pipeline

    @property
    def stack(self) -> Optional["StackResponse"]:
        """The `stack` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().stack

    @property
    def images(self) -> Dict[str, "BuildItem"]:
        """The `images` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().images

    @property
    def zenml_version(self) -> Optional[str]:
        """The `zenml_version` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().zenml_version

    @property
    def python_version(self) -> Optional[str]:
        """The `python_version` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().python_version

    @property
    def checksum(self) -> Optional[str]:
        """The `checksum` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().checksum

    @property
    def stack_checksum(self) -> Optional[str]:
        """The `stack_checksum` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().stack_checksum

    @property
    def is_local(self) -> bool:
        """The `is_local` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().is_local

    @property
    def contains_code(self) -> bool:
        """The `contains_code` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().contains_code

`checksum` `property`

The checksum property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`contains_code` `property`

The contains_code property.

Returns:

Type	Description
`bool`	the value of the property.

`images` `property`

The images property.

Returns:

Type	Description
`Dict[str, BuildItem]`	the value of the property.

`is_local` `property`

The is_local property.

Returns:

Type	Description
`bool`	the value of the property.

`pipeline` `property`

The pipeline property.

Returns:

Type	Description
`Optional[PipelineResponse]`	the value of the property.

`python_version` `property`

The python_version property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`requires_code_download` `property`

Whether the build requires code download.

Returns:

Type	Description
`bool`	Whether the build requires code download.

`stack` `property`

The stack property.

Returns:

Type	Description
`Optional[StackResponse]`	the value of the property.

`stack_checksum` `property`

The stack_checksum property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`zenml_version` `property`

The zenml_version property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`get_hydrated_version()`

Return the hydrated version of this pipeline build.

Returns:

Type	Description
`PipelineBuildResponse`	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/pipeline_build.py

def get_hydrated_version(self) -> "PipelineBuildResponse":
    """Return the hydrated version of this pipeline build.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_build(self.id)

`get_image(component_key, step=None)`

Get the image built for a specific key.

Parameters:

Name	Type	Description	Default
`component_key`	`str`	The key for which to get the image.	required
`step`	`Optional[str]`	The pipeline step for which to get the image. If no image exists for this step, will fall back to the pipeline image for the same key.	`None`

Returns:

Type	Description
`str`	The image name or digest.

Source code in src/zenml/models/v2/core/pipeline_build.py

def get_image(self, component_key: str, step: Optional[str] = None) -> str:
    """Get the image built for a specific key.

    Args:
        component_key: The key for which to get the image.
        step: The pipeline step for which to get the image. If no image
            exists for this step, will fall back to the pipeline image for
            the same key.

    Returns:
        The image name or digest.
    """
    return self._get_item(component_key=component_key, step=step).image

`get_image_key(component_key, step=None)` `staticmethod`

Get the image key.

Parameters:

Name	Type	Description	Default
`component_key`	`str`	The component key.	required
`step`	`Optional[str]`	The pipeline step for which the image was built.	`None`

Returns:

Type	Description
`str`	The image key.

Source code in src/zenml/models/v2/core/pipeline_build.py

@staticmethod
def get_image_key(component_key: str, step: Optional[str] = None) -> str:
    """Get the image key.

    Args:
        component_key: The component key.
        step: The pipeline step for which the image was built.

    Returns:
        The image key.
    """
    if step:
        return f"{step}.{component_key}"
    else:
        return component_key

`get_settings_checksum(component_key, step=None)`

Get the settings checksum for a specific key.

Parameters:

Name	Type	Description	Default
`component_key`	`str`	The key for which to get the checksum.	required
`step`	`Optional[str]`	The pipeline step for which to get the checksum. If no image exists for this step, will fall back to the pipeline image for the same key.	`None`

Returns:

Type	Description
`Optional[str]`	The settings checksum.

Source code in src/zenml/models/v2/core/pipeline_build.py

def get_settings_checksum(
    self, component_key: str, step: Optional[str] = None
) -> Optional[str]:
    """Get the settings checksum for a specific key.

    Args:
        component_key: The key for which to get the checksum.
        step: The pipeline step for which to get the checksum. If no
            image exists for this step, will fall back to the pipeline image
            for the same key.

    Returns:
        The settings checksum.
    """
    return self._get_item(
        component_key=component_key, step=step
    ).settings_checksum

`to_yaml()`

Create a yaml representation of the pipeline build.

Create a yaml representation of the pipeline build that can be used to create a PipelineBuildBase instance.

Returns:

Type	Description
`Dict[str, Any]`	The yaml representation of the pipeline build.

Source code in src/zenml/models/v2/core/pipeline_build.py

def to_yaml(self) -> Dict[str, Any]:
    """Create a yaml representation of the pipeline build.

    Create a yaml representation of the pipeline build that can be used
    to create a PipelineBuildBase instance.

    Returns:
        The yaml representation of the pipeline build.
    """
    # Get the base attributes
    yaml_dict: Dict[str, Any] = json.loads(
        self.model_dump_json(
            exclude={
                "body",
                "metadata",
            }
        )
    )
    images = json.loads(
        self.get_metadata().model_dump_json(
            exclude={
                "pipeline",
                "stack",
                "workspace",
            }
        )
    )
    yaml_dict.update(images)
    return yaml_dict

`PipelineBuildResponseBody`

Response body for pipeline builds.

Source code in src/zenml/models/v2/core/pipeline_build.py

class PipelineBuildResponseBody(WorkspaceScopedResponseBody):
    """Response body for pipeline builds."""

`PipelineBuildResponseMetadata`

Response metadata for pipeline builds.

Source code in src/zenml/models/v2/core/pipeline_build.py

class PipelineBuildResponseMetadata(WorkspaceScopedResponseMetadata):
    """Response metadata for pipeline builds."""

    pipeline: Optional["PipelineResponse"] = Field(
        default=None, title="The pipeline that was used for this build."
    )
    stack: Optional["StackResponse"] = Field(
        default=None, title="The stack that was used for this build."
    )
    images: Dict[str, "BuildItem"] = Field(
        default={}, title="The images of this build."
    )
    zenml_version: Optional[str] = Field(
        default=None, title="The version of ZenML used for this build."
    )
    python_version: Optional[str] = Field(
        default=None, title="The Python version used for this build."
    )
    checksum: Optional[str] = Field(default=None, title="The build checksum.")
    stack_checksum: Optional[str] = Field(
        default=None, title="The stack checksum."
    )
    is_local: bool = Field(
        title="Whether the build images are stored in a container "
        "registry or locally.",
    )
    contains_code: bool = Field(
        title="Whether any image of the build contains user code.",
    )

`PipelineDeploymentBase`

Bases: BaseZenModel

Base model for pipeline deployments.

Source code in src/zenml/models/v2/core/pipeline_deployment.py

class PipelineDeploymentBase(BaseZenModel):
    """Base model for pipeline deployments."""

    run_name_template: str = Field(
        title="The run name template for runs created using this deployment.",
    )
    pipeline_configuration: PipelineConfiguration = Field(
        title="The pipeline configuration for this deployment."
    )
    step_configurations: Dict[str, Step] = Field(
        default={}, title="The step configurations for this deployment."
    )
    client_environment: Dict[str, str] = Field(
        default={}, title="The client environment for this deployment."
    )
    client_version: Optional[str] = Field(
        default=None,
        title="The version of the ZenML installation on the client side.",
    )
    server_version: Optional[str] = Field(
        default=None,
        title="The version of the ZenML installation on the server side.",
    )
    pipeline_version_hash: Optional[str] = Field(
        default=None,
        title="The pipeline version hash of the deployment.",
    )
    pipeline_spec: Optional[PipelineSpec] = Field(
        default=None,
        title="The pipeline spec of the deployment.",
    )

    @property
    def should_prevent_build_reuse(self) -> bool:
        """Whether the deployment prevents a build reuse.

        Returns:
            Whether the deployment prevents a build reuse.
        """
        return any(
            step.config.docker_settings.prevent_build_reuse
            for step in self.step_configurations.values()
        )

`should_prevent_build_reuse` `property`

Whether the deployment prevents a build reuse.

Returns:

Type	Description
`bool`	Whether the deployment prevents a build reuse.

`PipelineDeploymentFilter`

Model to enable advanced filtering of all pipeline deployments.

Source code in src/zenml/models/v2/core/pipeline_deployment.py

class PipelineDeploymentFilter(WorkspaceScopedFilter):
    """Model to enable advanced filtering of all pipeline deployments."""

    pipeline_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Pipeline associated with the deployment.",
        union_mode="left_to_right",
    )
    stack_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Stack associated with the deployment.",
        union_mode="left_to_right",
    )
    build_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Build associated with the deployment.",
        union_mode="left_to_right",
    )
    schedule_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Schedule associated with the deployment.",
        union_mode="left_to_right",
    )
    template_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Template used as base for the deployment.",
        union_mode="left_to_right",
    )

`PipelineDeploymentRequest`

Bases: PipelineDeploymentBase, WorkspaceScopedRequest

Request model for pipeline deployments.

Source code in src/zenml/models/v2/core/pipeline_deployment.py

class PipelineDeploymentRequest(
    PipelineDeploymentBase, WorkspaceScopedRequest
):
    """Request model for pipeline deployments."""

    stack: UUID = Field(title="The stack associated with the deployment.")
    pipeline: Optional[UUID] = Field(
        default=None, title="The pipeline associated with the deployment."
    )
    build: Optional[UUID] = Field(
        default=None, title="The build associated with the deployment."
    )
    schedule: Optional[UUID] = Field(
        default=None, title="The schedule associated with the deployment."
    )
    code_reference: Optional["CodeReferenceRequest"] = Field(
        default=None,
        title="The code reference associated with the deployment.",
    )
    code_path: Optional[str] = Field(
        default=None,
        title="Optional path where the code is stored in the artifact store.",
    )
    template: Optional[UUID] = Field(
        default=None,
        description="Template used for the deployment.",
    )

`PipelineDeploymentResponse`

Bases: WorkspaceScopedResponse[PipelineDeploymentResponseBody, PipelineDeploymentResponseMetadata, PipelineDeploymentResponseResources]

Response model for pipeline deployments.

Source code in src/zenml/models/v2/core/pipeline_deployment.py

class PipelineDeploymentResponse(
    WorkspaceScopedResponse[
        PipelineDeploymentResponseBody,
        PipelineDeploymentResponseMetadata,
        PipelineDeploymentResponseResources,
    ]
):
    """Response model for pipeline deployments."""

    def get_hydrated_version(self) -> "PipelineDeploymentResponse":
        """Return the hydrated version of this pipeline deployment.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_deployment(self.id)

    # Body and metadata properties
    @property
    def run_name_template(self) -> str:
        """The `run_name_template` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().run_name_template

    @property
    def pipeline_configuration(self) -> PipelineConfiguration:
        """The `pipeline_configuration` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().pipeline_configuration

    @property
    def step_configurations(self) -> Dict[str, Step]:
        """The `step_configurations` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().step_configurations

    @property
    def client_environment(self) -> Dict[str, str]:
        """The `client_environment` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().client_environment

    @property
    def client_version(self) -> Optional[str]:
        """The `client_version` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().client_version

    @property
    def server_version(self) -> Optional[str]:
        """The `server_version` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().server_version

    @property
    def pipeline_version_hash(self) -> Optional[str]:
        """The `pipeline_version_hash` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().pipeline_version_hash

    @property
    def pipeline_spec(self) -> Optional[PipelineSpec]:
        """The `pipeline_spec` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().pipeline_spec

    @property
    def code_path(self) -> Optional[str]:
        """The `code_path` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().code_path

    @property
    def pipeline(self) -> Optional[PipelineResponse]:
        """The `pipeline` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().pipeline

    @property
    def stack(self) -> Optional[StackResponse]:
        """The `stack` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().stack

    @property
    def build(self) -> Optional[PipelineBuildResponse]:
        """The `build` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().build

    @property
    def schedule(self) -> Optional[ScheduleResponse]:
        """The `schedule` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().schedule

    @property
    def code_reference(self) -> Optional[CodeReferenceResponse]:
        """The `code_reference` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().code_reference

    @property
    def template_id(self) -> Optional[UUID]:
        """The `template_id` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().template_id

`build` `property`

The build property.

Returns:

Type	Description
`Optional[PipelineBuildResponse]`	the value of the property.

`client_environment` `property`

The client_environment property.

Returns:

Type	Description
`Dict[str, str]`	the value of the property.

`client_version` `property`

The client_version property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`code_path` `property`

The code_path property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`code_reference` `property`

The code_reference property.

Returns:

Type	Description
`Optional[CodeReferenceResponse]`	the value of the property.

`pipeline` `property`

The pipeline property.

Returns:

Type	Description
`Optional[PipelineResponse]`	the value of the property.

`pipeline_configuration` `property`

The pipeline_configuration property.

Returns:

Type	Description
`PipelineConfiguration`	the value of the property.

`pipeline_spec` `property`

The pipeline_spec property.

Returns:

Type	Description
`Optional[PipelineSpec]`	the value of the property.

`pipeline_version_hash` `property`

The pipeline_version_hash property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`run_name_template` `property`

The run_name_template property.

Returns:

Type	Description
`str`	the value of the property.

`schedule` `property`

The schedule property.

Returns:

Type	Description
`Optional[ScheduleResponse]`	the value of the property.

`server_version` `property`

The server_version property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`stack` `property`

The stack property.

Returns:

Type	Description
`Optional[StackResponse]`	the value of the property.

`step_configurations` `property`

The step_configurations property.

Returns:

Type	Description
`Dict[str, Step]`	the value of the property.

`template_id` `property`

The template_id property.

Returns:

Type	Description
`Optional[UUID]`	the value of the property.

`get_hydrated_version()`

Return the hydrated version of this pipeline deployment.

Returns:

Type	Description
`PipelineDeploymentResponse`	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/pipeline_deployment.py

def get_hydrated_version(self) -> "PipelineDeploymentResponse":
    """Return the hydrated version of this pipeline deployment.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_deployment(self.id)

`PipelineDeploymentResponseBody`

Response body for pipeline deployments.

Source code in src/zenml/models/v2/core/pipeline_deployment.py

class PipelineDeploymentResponseBody(WorkspaceScopedResponseBody):
    """Response body for pipeline deployments."""

`PipelineDeploymentResponseMetadata`

Response metadata for pipeline deployments.

Source code in src/zenml/models/v2/core/pipeline_deployment.py

class PipelineDeploymentResponseMetadata(WorkspaceScopedResponseMetadata):
    """Response metadata for pipeline deployments."""

    run_name_template: str = Field(
        title="The run name template for runs created using this deployment.",
    )
    pipeline_configuration: PipelineConfiguration = Field(
        title="The pipeline configuration for this deployment."
    )
    step_configurations: Dict[str, Step] = Field(
        default={}, title="The step configurations for this deployment."
    )
    client_environment: Dict[str, str] = Field(
        default={}, title="The client environment for this deployment."
    )
    client_version: Optional[str] = Field(
        title="The version of the ZenML installation on the client side."
    )
    server_version: Optional[str] = Field(
        title="The version of the ZenML installation on the server side."
    )
    pipeline_version_hash: Optional[str] = Field(
        default=None, title="The pipeline version hash of the deployment."
    )
    pipeline_spec: Optional[PipelineSpec] = Field(
        default=None, title="The pipeline spec of the deployment."
    )
    code_path: Optional[str] = Field(
        default=None,
        title="Optional path where the code is stored in the artifact store.",
    )

    pipeline: Optional[PipelineResponse] = Field(
        default=None, title="The pipeline associated with the deployment."
    )
    stack: Optional[StackResponse] = Field(
        default=None, title="The stack associated with the deployment."
    )
    build: Optional[PipelineBuildResponse] = Field(
        default=None,
        title="The pipeline build associated with the deployment.",
    )
    schedule: Optional[ScheduleResponse] = Field(
        default=None, title="The schedule associated with the deployment."
    )
    code_reference: Optional[CodeReferenceResponse] = Field(
        default=None,
        title="The code reference associated with the deployment.",
    )
    template_id: Optional[UUID] = Field(
        default=None,
        description="Template used for the pipeline run.",
    )

`PipelineFilter`

Pipeline filter model.

Source code in src/zenml/models/v2/core/pipeline.py

class PipelineFilter(WorkspaceScopedFilter, TaggableFilter):
    """Pipeline filter model."""

    CUSTOM_SORTING_OPTIONS: ClassVar[List[str]] = [
        *WorkspaceScopedFilter.CUSTOM_SORTING_OPTIONS,
        *TaggableFilter.CUSTOM_SORTING_OPTIONS,
        SORT_PIPELINES_BY_LATEST_RUN_KEY,
    ]
    FILTER_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        *WorkspaceScopedFilter.FILTER_EXCLUDE_FIELDS,
        *TaggableFilter.FILTER_EXCLUDE_FIELDS,
        "latest_run_status",
    ]
    CLI_EXCLUDE_FIELDS = [
        *WorkspaceScopedFilter.CLI_EXCLUDE_FIELDS,
        *TaggableFilter.CLI_EXCLUDE_FIELDS,
    ]

    name: Optional[str] = Field(
        default=None,
        description="Name of the Pipeline",
    )
    latest_run_status: Optional[str] = Field(
        default=None,
        description="Filter by the status of the latest run of a pipeline. "
        "This will always be applied as an `AND` filter for now.",
    )

    def apply_filter(
        self, query: AnyQuery, table: Type["AnySchema"]
    ) -> AnyQuery:
        """Applies the filter to a query.

        Args:
            query: The query to which to apply the filter.
            table: The query table.

        Returns:
            The query with filter applied.
        """
        query = super().apply_filter(query, table)

        from sqlmodel import and_, col, func, select

        from zenml.zen_stores.schemas import PipelineRunSchema, PipelineSchema

        if self.latest_run_status:
            latest_pipeline_run_subquery = (
                select(
                    PipelineRunSchema.pipeline_id,
                    func.max(PipelineRunSchema.created).label("created"),
                )
                .where(col(PipelineRunSchema.pipeline_id).is_not(None))
                .group_by(col(PipelineRunSchema.pipeline_id))
                .subquery()
            )

            query = (
                query.join(
                    PipelineRunSchema,
                    PipelineSchema.id == PipelineRunSchema.pipeline_id,
                )
                .join(
                    latest_pipeline_run_subquery,
                    and_(
                        PipelineRunSchema.pipeline_id
                        == latest_pipeline_run_subquery.c.pipeline_id,
                        PipelineRunSchema.created
                        == latest_pipeline_run_subquery.c.created,
                    ),
                )
                .where(
                    self.generate_custom_query_conditions_for_column(
                        value=self.latest_run_status,
                        table=PipelineRunSchema,
                        column="status",
                    )
                )
            )

        return query

    def apply_sorting(
        self,
        query: AnyQuery,
        table: Type["AnySchema"],
    ) -> AnyQuery:
        """Apply sorting to the query.

        Args:
            query: The query to which to apply the sorting.
            table: The query table.

        Returns:
            The query with sorting applied.
        """
        from sqlmodel import asc, case, col, desc, func, select

        from zenml.enums import SorterOps
        from zenml.zen_stores.schemas import PipelineRunSchema, PipelineSchema

        sort_by, operand = self.sorting_params

        if sort_by == SORT_PIPELINES_BY_LATEST_RUN_KEY:
            # Subquery to find the latest run per pipeline
            latest_run_subquery = (
                select(
                    PipelineSchema.id,
                    case(
                        (
                            func.max(PipelineRunSchema.created).is_(None),
                            PipelineSchema.created,
                        ),
                        else_=func.max(PipelineRunSchema.created),
                    ).label("latest_run"),
                )
                .outerjoin(
                    PipelineRunSchema,
                    PipelineSchema.id == PipelineRunSchema.pipeline_id,  # type: ignore[arg-type]
                )
                .group_by(col(PipelineSchema.id))
                .subquery()
            )

            query = query.add_columns(
                latest_run_subquery.c.latest_run,
            ).where(PipelineSchema.id == latest_run_subquery.c.id)

            if operand == SorterOps.ASCENDING:
                query = query.order_by(
                    asc(latest_run_subquery.c.latest_run),
                    asc(PipelineSchema.id),
                )
            else:
                query = query.order_by(
                    desc(latest_run_subquery.c.latest_run),
                    desc(PipelineSchema.id),
                )
            return query
        else:
            return super().apply_sorting(query=query, table=table)

`apply_filter(query, table)`

Applies the filter to a query.

Parameters:

Name	Type	Description	Default
`query`	`AnyQuery`	The query to which to apply the filter.	required
`table`	`Type[AnySchema]`	The query table.	required

Returns:

Type	Description
`AnyQuery`	The query with filter applied.

Source code in src/zenml/models/v2/core/pipeline.py

def apply_filter(
    self, query: AnyQuery, table: Type["AnySchema"]
) -> AnyQuery:
    """Applies the filter to a query.

    Args:
        query: The query to which to apply the filter.
        table: The query table.

    Returns:
        The query with filter applied.
    """
    query = super().apply_filter(query, table)

    from sqlmodel import and_, col, func, select

    from zenml.zen_stores.schemas import PipelineRunSchema, PipelineSchema

    if self.latest_run_status:
        latest_pipeline_run_subquery = (
            select(
                PipelineRunSchema.pipeline_id,
                func.max(PipelineRunSchema.created).label("created"),
            )
            .where(col(PipelineRunSchema.pipeline_id).is_not(None))
            .group_by(col(PipelineRunSchema.pipeline_id))
            .subquery()
        )

        query = (
            query.join(
                PipelineRunSchema,
                PipelineSchema.id == PipelineRunSchema.pipeline_id,
            )
            .join(
                latest_pipeline_run_subquery,
                and_(
                    PipelineRunSchema.pipeline_id
                    == latest_pipeline_run_subquery.c.pipeline_id,
                    PipelineRunSchema.created
                    == latest_pipeline_run_subquery.c.created,
                ),
            )
            .where(
                self.generate_custom_query_conditions_for_column(
                    value=self.latest_run_status,
                    table=PipelineRunSchema,
                    column="status",
                )
            )
        )

    return query

`apply_sorting(query, table)`

Apply sorting to the query.

Parameters:

Name	Type	Description	Default
`query`	`AnyQuery`	The query to which to apply the sorting.	required
`table`	`Type[AnySchema]`	The query table.	required

Returns:

Type	Description
`AnyQuery`	The query with sorting applied.

Source code in src/zenml/models/v2/core/pipeline.py

def apply_sorting(
    self,
    query: AnyQuery,
    table: Type["AnySchema"],
) -> AnyQuery:
    """Apply sorting to the query.

    Args:
        query: The query to which to apply the sorting.
        table: The query table.

    Returns:
        The query with sorting applied.
    """
    from sqlmodel import asc, case, col, desc, func, select

    from zenml.enums import SorterOps
    from zenml.zen_stores.schemas import PipelineRunSchema, PipelineSchema

    sort_by, operand = self.sorting_params

    if sort_by == SORT_PIPELINES_BY_LATEST_RUN_KEY:
        # Subquery to find the latest run per pipeline
        latest_run_subquery = (
            select(
                PipelineSchema.id,
                case(
                    (
                        func.max(PipelineRunSchema.created).is_(None),
                        PipelineSchema.created,
                    ),
                    else_=func.max(PipelineRunSchema.created),
                ).label("latest_run"),
            )
            .outerjoin(
                PipelineRunSchema,
                PipelineSchema.id == PipelineRunSchema.pipeline_id,  # type: ignore[arg-type]
            )
            .group_by(col(PipelineSchema.id))
            .subquery()
        )

        query = query.add_columns(
            latest_run_subquery.c.latest_run,
        ).where(PipelineSchema.id == latest_run_subquery.c.id)

        if operand == SorterOps.ASCENDING:
            query = query.order_by(
                asc(latest_run_subquery.c.latest_run),
                asc(PipelineSchema.id),
            )
        else:
            query = query.order_by(
                desc(latest_run_subquery.c.latest_run),
                desc(PipelineSchema.id),
            )
        return query
    else:
        return super().apply_sorting(query=query, table=table)

`PipelineRequest`

Request model for pipelines.

Source code in src/zenml/models/v2/core/pipeline.py

class PipelineRequest(WorkspaceScopedRequest):
    """Request model for pipelines."""

    name: str = Field(
        title="The name of the pipeline.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    description: Optional[str] = Field(
        default=None,
        title="The description of the pipeline.",
        max_length=TEXT_FIELD_MAX_LENGTH,
    )
    tags: Optional[List[str]] = Field(
        default=None,
        title="Tags of the pipeline.",
    )

`PipelineResponse`

Bases: WorkspaceScopedResponse[PipelineResponseBody, PipelineResponseMetadata, PipelineResponseResources]

Response model for pipelines.

Source code in src/zenml/models/v2/core/pipeline.py

class PipelineResponse(
    WorkspaceScopedResponse[
        PipelineResponseBody,
        PipelineResponseMetadata,
        PipelineResponseResources,
    ]
):
    """Response model for pipelines."""

    name: str = Field(
        title="The name of the pipeline.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    def get_hydrated_version(self) -> "PipelineResponse":
        """Get the hydrated version of this pipeline.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_pipeline(self.id)

    # Helper methods
    def get_runs(self, **kwargs: Any) -> List["PipelineRunResponse"]:
        """Get runs of this pipeline.

        Can be used to fetch runs other than `self.runs` and supports
        fine-grained filtering and pagination.

        Args:
            **kwargs: Further arguments for filtering or pagination that are
                passed to `client.list_pipeline_runs()`.

        Returns:
            List of runs of this pipeline.
        """
        from zenml.client import Client

        return Client().list_pipeline_runs(pipeline_id=self.id, **kwargs).items

    @property
    def runs(self) -> List["PipelineRunResponse"]:
        """Returns the 20 most recent runs of this pipeline in descending order.

        Returns:
            The 20 most recent runs of this pipeline in descending order.
        """
        return self.get_runs()

    @property
    def num_runs(self) -> int:
        """Returns the number of runs of this pipeline.

        Returns:
            The number of runs of this pipeline.
        """
        from zenml.client import Client

        return Client().list_pipeline_runs(pipeline_id=self.id, size=1).total

    @property
    def last_run(self) -> "PipelineRunResponse":
        """Returns the last run of this pipeline.

        Returns:
            The last run of this pipeline.

        Raises:
            RuntimeError: If no runs were found for this pipeline.
        """
        runs = self.get_runs(size=1)
        if not runs:
            raise RuntimeError(
                f"No runs found for pipeline '{self.name}' with id {self.id}."
            )
        return runs[0]

    @property
    def last_successful_run(self) -> "PipelineRunResponse":
        """Returns the last successful run of this pipeline.

        Returns:
            The last successful run of this pipeline.

        Raises:
            RuntimeError: If no successful runs were found for this pipeline.
        """
        runs = self.get_runs(status=ExecutionStatus.COMPLETED, size=1)
        if not runs:
            raise RuntimeError(
                f"No successful runs found for pipeline '{self.name}' with id "
                f"{self.id}."
            )
        return runs[0]

    @property
    def latest_run_id(self) -> Optional[UUID]:
        """The `latest_run_id` property.

        Returns:
            the value of the property.
        """
        return self.get_body().latest_run_id

    @property
    def latest_run_status(self) -> Optional[ExecutionStatus]:
        """The `latest_run_status` property.

        Returns:
            the value of the property.
        """
        return self.get_body().latest_run_status

    @property
    def tags(self) -> List[TagResponse]:
        """The `tags` property.

        Returns:
            the value of the property.
        """
        return self.get_resources().tags

`last_run` `property`

Returns the last run of this pipeline.

Returns:

Type	Description
`PipelineRunResponse`	The last run of this pipeline.

Raises:

Type	Description
`RuntimeError`	If no runs were found for this pipeline.

`last_successful_run` `property`

Returns the last successful run of this pipeline.

Returns:

Type	Description
`PipelineRunResponse`	The last successful run of this pipeline.

Raises:

Type	Description
`RuntimeError`	If no successful runs were found for this pipeline.

`latest_run_id` `property`

The latest_run_id property.

Returns:

Type	Description
`Optional[UUID]`	the value of the property.

`latest_run_status` `property`

The latest_run_status property.

Returns:

Type	Description
`Optional[ExecutionStatus]`	the value of the property.

`num_runs` `property`

Returns the number of runs of this pipeline.

Returns:

Type	Description
`int`	The number of runs of this pipeline.

`runs` `property`

Returns the 20 most recent runs of this pipeline in descending order.

Returns:

Type	Description
`List[PipelineRunResponse]`	The 20 most recent runs of this pipeline in descending order.

`tags` `property`

The tags property.

Returns:

Type	Description
`List[TagResponse]`	the value of the property.

`get_hydrated_version()`

Get the hydrated version of this pipeline.

Returns:

Type	Description
`PipelineResponse`	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/pipeline.py

def get_hydrated_version(self) -> "PipelineResponse":
    """Get the hydrated version of this pipeline.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_pipeline(self.id)

`get_runs(**kwargs)`

Get runs of this pipeline.

Can be used to fetch runs other than self.runs and supports fine-grained filtering and pagination.

Parameters:

Name	Type	Description	Default
`**kwargs`	`Any`	Further arguments for filtering or pagination that are passed to `client.list_pipeline_runs()`.	`{}`

Returns:

Type	Description
`List[PipelineRunResponse]`	List of runs of this pipeline.

Source code in src/zenml/models/v2/core/pipeline.py

def get_runs(self, **kwargs: Any) -> List["PipelineRunResponse"]:
    """Get runs of this pipeline.

    Can be used to fetch runs other than `self.runs` and supports
    fine-grained filtering and pagination.

    Args:
        **kwargs: Further arguments for filtering or pagination that are
            passed to `client.list_pipeline_runs()`.

    Returns:
        List of runs of this pipeline.
    """
    from zenml.client import Client

    return Client().list_pipeline_runs(pipeline_id=self.id, **kwargs).items

`PipelineResponseBody`

Response body for pipelines.

Source code in src/zenml/models/v2/core/pipeline.py

class PipelineResponseBody(WorkspaceScopedResponseBody):
    """Response body for pipelines."""

    latest_run_id: Optional[UUID] = Field(
        default=None,
        title="The ID of the latest run of the pipeline.",
    )
    latest_run_status: Optional[ExecutionStatus] = Field(
        default=None,
        title="The status of the latest run of the pipeline.",
    )

`PipelineResponseMetadata`

Response metadata for pipelines.

Source code in src/zenml/models/v2/core/pipeline.py

class PipelineResponseMetadata(WorkspaceScopedResponseMetadata):
    """Response metadata for pipelines."""

    description: Optional[str] = Field(
        default=None,
        title="The description of the pipeline.",
    )

`PipelineResponseResources`

Class for all resource models associated with the pipeline entity.

Source code in src/zenml/models/v2/core/pipeline.py

class PipelineResponseResources(WorkspaceScopedResponseResources):
    """Class for all resource models associated with the pipeline entity."""

    latest_run_user: Optional["UserResponse"] = Field(
        default=None,
        title="The user that created the latest run of this pipeline.",
    )
    tags: List[TagResponse] = Field(
        title="Tags associated with the pipeline.",
    )

`PipelineRunFilter`

Model to enable advanced filtering of all Workspaces.

Source code in src/zenml/models/v2/core/pipeline_run.py

class PipelineRunFilter(WorkspaceScopedFilter, TaggableFilter):
    """Model to enable advanced filtering of all Workspaces."""

    CUSTOM_SORTING_OPTIONS: ClassVar[List[str]] = [
        *WorkspaceScopedFilter.CUSTOM_SORTING_OPTIONS,
        *TaggableFilter.CUSTOM_SORTING_OPTIONS,
        "tag",
        "stack",
        "pipeline",
        "model",
        "model_version",
    ]
    FILTER_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        *WorkspaceScopedFilter.FILTER_EXCLUDE_FIELDS,
        *TaggableFilter.FILTER_EXCLUDE_FIELDS,
        "unlisted",
        "code_repository_id",
        "build_id",
        "schedule_id",
        "stack_id",
        "template_id",
        "pipeline",
        "stack",
        "code_repository",
        "model",
        "stack_component",
        "pipeline_name",
        "templatable",
        "run_metadata",
    ]
    CLI_EXCLUDE_FIELDS = [
        *WorkspaceScopedFilter.CLI_EXCLUDE_FIELDS,
        *TaggableFilter.CLI_EXCLUDE_FIELDS,
    ]

    name: Optional[str] = Field(
        default=None,
        description="Name of the Pipeline Run",
    )
    orchestrator_run_id: Optional[str] = Field(
        default=None,
        description="Name of the Pipeline Run within the orchestrator",
    )
    pipeline_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Pipeline associated with the Pipeline Run",
        union_mode="left_to_right",
    )
    stack_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Stack used for the Pipeline Run",
        union_mode="left_to_right",
    )
    schedule_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Schedule that triggered the Pipeline Run",
        union_mode="left_to_right",
    )
    build_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Build used for the Pipeline Run",
        union_mode="left_to_right",
    )
    deployment_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Deployment used for the Pipeline Run",
        union_mode="left_to_right",
    )
    code_repository_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Code repository used for the Pipeline Run",
        union_mode="left_to_right",
    )
    template_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Template used for the pipeline run.",
        union_mode="left_to_right",
    )
    model_version_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Model version associated with the pipeline run.",
        union_mode="left_to_right",
    )
    status: Optional[str] = Field(
        default=None,
        description="Name of the Pipeline Run",
    )
    start_time: Optional[Union[datetime, str]] = Field(
        default=None,
        description="Start time for this run",
        union_mode="left_to_right",
    )
    end_time: Optional[Union[datetime, str]] = Field(
        default=None,
        description="End time for this run",
        union_mode="left_to_right",
    )
    unlisted: Optional[bool] = None
    run_metadata: Optional[Dict[str, Any]] = Field(
        default=None,
        description="The run_metadata to filter the pipeline runs by.",
    )
    # TODO: Remove once frontend is ready for it. This is replaced by the more
    #   generic `pipeline` filter below.
    pipeline_name: Optional[str] = Field(
        default=None,
        description="Name of the pipeline associated with the run",
    )
    pipeline: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Name/ID of the pipeline associated with the run.",
    )
    stack: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Name/ID of the stack associated with the run.",
    )
    code_repository: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Name/ID of the code repository associated with the run.",
    )
    model: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Name/ID of the model associated with the run.",
    )
    stack_component: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Name/ID of the stack component associated with the run.",
    )
    templatable: Optional[bool] = Field(
        default=None, description="Whether the run is templatable."
    )
    model_config = ConfigDict(protected_namespaces=())

    def get_custom_filters(
        self,
        table: Type["AnySchema"],
    ) -> List["ColumnElement[bool]"]:
        """Get custom filters.

        Args:
            table: The query table.

        Returns:
            A list of custom filters.
        """
        custom_filters = super().get_custom_filters(table)

        from sqlmodel import and_, col, or_

        from zenml.zen_stores.schemas import (
            CodeReferenceSchema,
            CodeRepositorySchema,
            ModelSchema,
            ModelVersionSchema,
            PipelineBuildSchema,
            PipelineDeploymentSchema,
            PipelineRunSchema,
            PipelineSchema,
            RunMetadataResourceSchema,
            RunMetadataSchema,
            ScheduleSchema,
            StackComponentSchema,
            StackCompositionSchema,
            StackSchema,
        )

        if self.unlisted is not None:
            if self.unlisted is True:
                unlisted_filter = PipelineRunSchema.pipeline_id.is_(None)  # type: ignore[union-attr]
            else:
                unlisted_filter = PipelineRunSchema.pipeline_id.is_not(None)  # type: ignore[union-attr]
            custom_filters.append(unlisted_filter)

        if self.code_repository_id:
            code_repo_filter = and_(
                PipelineRunSchema.deployment_id == PipelineDeploymentSchema.id,
                PipelineDeploymentSchema.code_reference_id
                == CodeReferenceSchema.id,
                CodeReferenceSchema.code_repository_id
                == self.code_repository_id,
            )
            custom_filters.append(code_repo_filter)

        if self.stack_id:
            stack_filter = and_(
                PipelineRunSchema.deployment_id == PipelineDeploymentSchema.id,
                PipelineDeploymentSchema.stack_id == StackSchema.id,
                StackSchema.id == self.stack_id,
            )
            custom_filters.append(stack_filter)

        if self.schedule_id:
            schedule_filter = and_(
                PipelineRunSchema.deployment_id == PipelineDeploymentSchema.id,
                PipelineDeploymentSchema.schedule_id == ScheduleSchema.id,
                ScheduleSchema.id == self.schedule_id,
            )
            custom_filters.append(schedule_filter)

        if self.build_id:
            pipeline_build_filter = and_(
                PipelineRunSchema.deployment_id == PipelineDeploymentSchema.id,
                PipelineDeploymentSchema.build_id == PipelineBuildSchema.id,
                PipelineBuildSchema.id == self.build_id,
            )
            custom_filters.append(pipeline_build_filter)

        if self.template_id:
            run_template_filter = and_(
                PipelineRunSchema.deployment_id == PipelineDeploymentSchema.id,
                PipelineDeploymentSchema.template_id == self.template_id,
            )
            custom_filters.append(run_template_filter)

        if self.pipeline:
            pipeline_filter = and_(
                PipelineRunSchema.pipeline_id == PipelineSchema.id,
                self.generate_name_or_id_query_conditions(
                    value=self.pipeline, table=PipelineSchema
                ),
            )
            custom_filters.append(pipeline_filter)

        if self.stack:
            stack_filter = and_(
                PipelineRunSchema.deployment_id == PipelineDeploymentSchema.id,
                PipelineDeploymentSchema.stack_id == StackSchema.id,
                self.generate_name_or_id_query_conditions(
                    value=self.stack,
                    table=StackSchema,
                ),
            )
            custom_filters.append(stack_filter)

        if self.code_repository:
            code_repo_filter = and_(
                PipelineRunSchema.deployment_id == PipelineDeploymentSchema.id,
                PipelineDeploymentSchema.code_reference_id
                == CodeReferenceSchema.id,
                CodeReferenceSchema.code_repository_id
                == CodeRepositorySchema.id,
                self.generate_name_or_id_query_conditions(
                    value=self.code_repository,
                    table=CodeRepositorySchema,
                ),
            )
            custom_filters.append(code_repo_filter)

        if self.model:
            model_filter = and_(
                PipelineRunSchema.model_version_id == ModelVersionSchema.id,
                ModelVersionSchema.model_id == ModelSchema.id,
                self.generate_name_or_id_query_conditions(
                    value=self.model, table=ModelSchema
                ),
            )
            custom_filters.append(model_filter)

        if self.stack_component:
            component_filter = and_(
                PipelineRunSchema.deployment_id == PipelineDeploymentSchema.id,
                PipelineDeploymentSchema.stack_id == StackSchema.id,
                StackSchema.id == StackCompositionSchema.stack_id,
                StackCompositionSchema.component_id == StackComponentSchema.id,
                self.generate_name_or_id_query_conditions(
                    value=self.stack_component,
                    table=StackComponentSchema,
                ),
            )
            custom_filters.append(component_filter)

        if self.pipeline_name:
            pipeline_name_filter = and_(
                PipelineRunSchema.pipeline_id == PipelineSchema.id,
                self.generate_custom_query_conditions_for_column(
                    value=self.pipeline_name,
                    table=PipelineSchema,
                    column="name",
                ),
            )
            custom_filters.append(pipeline_name_filter)

        if self.templatable is not None:
            if self.templatable is True:
                templatable_filter = and_(
                    # The following condition is not perfect as it does not
                    # consider stacks with custom flavor components or local
                    # components, but the best we can do currently with our
                    # table columns.
                    PipelineRunSchema.deployment_id
                    == PipelineDeploymentSchema.id,
                    PipelineDeploymentSchema.build_id
                    == PipelineBuildSchema.id,
                    col(PipelineBuildSchema.is_local).is_(False),
                    col(PipelineBuildSchema.stack_id).is_not(None),
                )
            else:
                templatable_filter = or_(
                    col(PipelineRunSchema.deployment_id).is_(None),
                    and_(
                        PipelineRunSchema.deployment_id
                        == PipelineDeploymentSchema.id,
                        col(PipelineDeploymentSchema.build_id).is_(None),
                    ),
                    and_(
                        PipelineRunSchema.deployment_id
                        == PipelineDeploymentSchema.id,
                        PipelineDeploymentSchema.build_id
                        == PipelineBuildSchema.id,
                        or_(
                            col(PipelineBuildSchema.is_local).is_(True),
                            col(PipelineBuildSchema.stack_id).is_(None),
                        ),
                    ),
                )

            custom_filters.append(templatable_filter)
        if self.run_metadata is not None:
            from zenml.enums import MetadataResourceTypes

            for key, value in self.run_metadata.items():
                additional_filter = and_(
                    RunMetadataResourceSchema.resource_id
                    == PipelineRunSchema.id,
                    RunMetadataResourceSchema.resource_type
                    == MetadataResourceTypes.PIPELINE_RUN.value,
                    RunMetadataResourceSchema.run_metadata_id
                    == RunMetadataSchema.id,
                    self.generate_custom_query_conditions_for_column(
                        value=key,
                        table=RunMetadataSchema,
                        column="key",
                    ),
                    self.generate_custom_query_conditions_for_column(
                        value=value,
                        table=RunMetadataSchema,
                        column="value",
                        json_encode_value=True,
                    ),
                )
                custom_filters.append(additional_filter)

        return custom_filters

    def apply_sorting(
        self,
        query: AnyQuery,
        table: Type["AnySchema"],
    ) -> AnyQuery:
        """Apply sorting to the query.

        Args:
            query: The query to which to apply the sorting.
            table: The query table.

        Returns:
            The query with sorting applied.
        """
        from sqlmodel import asc, desc

        from zenml.enums import SorterOps
        from zenml.zen_stores.schemas import (
            ModelSchema,
            ModelVersionSchema,
            PipelineDeploymentSchema,
            PipelineRunSchema,
            PipelineSchema,
            StackSchema,
        )

        sort_by, operand = self.sorting_params

        if sort_by == "pipeline":
            query = query.outerjoin(
                PipelineSchema,
                PipelineRunSchema.pipeline_id == PipelineSchema.id,
            )
            column = PipelineSchema.name
        elif sort_by == "stack":
            query = query.outerjoin(
                PipelineDeploymentSchema,
                PipelineRunSchema.deployment_id == PipelineDeploymentSchema.id,
            ).outerjoin(
                StackSchema,
                PipelineDeploymentSchema.stack_id == StackSchema.id,
            )
            column = StackSchema.name
        elif sort_by == "model":
            query = query.outerjoin(
                ModelVersionSchema,
                PipelineRunSchema.model_version_id == ModelVersionSchema.id,
            ).outerjoin(
                ModelSchema,
                ModelVersionSchema.model_id == ModelSchema.id,
            )
            column = ModelSchema.name
        elif sort_by == "model_version":
            query = query.outerjoin(
                ModelVersionSchema,
                PipelineRunSchema.model_version_id == ModelVersionSchema.id,
            )
            column = ModelVersionSchema.name
        else:
            return super().apply_sorting(query=query, table=table)

        query = query.add_columns(column)

        if operand == SorterOps.ASCENDING:
            query = query.order_by(asc(column))
        else:
            query = query.order_by(desc(column))

        return query

`apply_sorting(query, table)`

Apply sorting to the query.

Parameters:

Name	Type	Description	Default
`query`	`AnyQuery`	The query to which to apply the sorting.	required
`table`	`Type[AnySchema]`	The query table.	required

Returns:

Type	Description
`AnyQuery`	The query with sorting applied.

Source code in src/zenml/models/v2/core/pipeline_run.py

def apply_sorting(
    self,
    query: AnyQuery,
    table: Type["AnySchema"],
) -> AnyQuery:
    """Apply sorting to the query.

    Args:
        query: The query to which to apply the sorting.
        table: The query table.

    Returns:
        The query with sorting applied.
    """
    from sqlmodel import asc, desc

    from zenml.enums import SorterOps
    from zenml.zen_stores.schemas import (
        ModelSchema,
        ModelVersionSchema,
        PipelineDeploymentSchema,
        PipelineRunSchema,
        PipelineSchema,
        StackSchema,
    )

    sort_by, operand = self.sorting_params

    if sort_by == "pipeline":
        query = query.outerjoin(
            PipelineSchema,
            PipelineRunSchema.pipeline_id == PipelineSchema.id,
        )
        column = PipelineSchema.name
    elif sort_by == "stack":
        query = query.outerjoin(
            PipelineDeploymentSchema,
            PipelineRunSchema.deployment_id == PipelineDeploymentSchema.id,
        ).outerjoin(
            StackSchema,
            PipelineDeploymentSchema.stack_id == StackSchema.id,
        )
        column = StackSchema.name
    elif sort_by == "model":
        query = query.outerjoin(
            ModelVersionSchema,
            PipelineRunSchema.model_version_id == ModelVersionSchema.id,
        ).outerjoin(
            ModelSchema,
            ModelVersionSchema.model_id == ModelSchema.id,
        )
        column = ModelSchema.name
    elif sort_by == "model_version":
        query = query.outerjoin(
            ModelVersionSchema,
            PipelineRunSchema.model_version_id == ModelVersionSchema.id,
        )
        column = ModelVersionSchema.name
    else:
        return super().apply_sorting(query=query, table=table)

    query = query.add_columns(column)

    if operand == SorterOps.ASCENDING:
        query = query.order_by(asc(column))
    else:
        query = query.order_by(desc(column))

    return query

`get_custom_filters(table)`

Get custom filters.

Parameters:

Name	Type	Description	Default
`table`	`Type[AnySchema]`	The query table.	required

Returns:

Type	Description
`List[ColumnElement[bool]]`	A list of custom filters.

Source code in src/zenml/models/v2/core/pipeline_run.py

def get_custom_filters(
    self,
    table: Type["AnySchema"],
) -> List["ColumnElement[bool]"]:
    """Get custom filters.

    Args:
        table: The query table.

    Returns:
        A list of custom filters.
    """
    custom_filters = super().get_custom_filters(table)

    from sqlmodel import and_, col, or_

    from zenml.zen_stores.schemas import (
        CodeReferenceSchema,
        CodeRepositorySchema,
        ModelSchema,
        ModelVersionSchema,
        PipelineBuildSchema,
        PipelineDeploymentSchema,
        PipelineRunSchema,
        PipelineSchema,
        RunMetadataResourceSchema,
        RunMetadataSchema,
        ScheduleSchema,
        StackComponentSchema,
        StackCompositionSchema,
        StackSchema,
    )

    if self.unlisted is not None:
        if self.unlisted is True:
            unlisted_filter = PipelineRunSchema.pipeline_id.is_(None)  # type: ignore[union-attr]
        else:
            unlisted_filter = PipelineRunSchema.pipeline_id.is_not(None)  # type: ignore[union-attr]
        custom_filters.append(unlisted_filter)

    if self.code_repository_id:
        code_repo_filter = and_(
            PipelineRunSchema.deployment_id == PipelineDeploymentSchema.id,
            PipelineDeploymentSchema.code_reference_id
            == CodeReferenceSchema.id,
            CodeReferenceSchema.code_repository_id
            == self.code_repository_id,
        )
        custom_filters.append(code_repo_filter)

    if self.stack_id:
        stack_filter = and_(
            PipelineRunSchema.deployment_id == PipelineDeploymentSchema.id,
            PipelineDeploymentSchema.stack_id == StackSchema.id,
            StackSchema.id == self.stack_id,
        )
        custom_filters.append(stack_filter)

    if self.schedule_id:
        schedule_filter = and_(
            PipelineRunSchema.deployment_id == PipelineDeploymentSchema.id,
            PipelineDeploymentSchema.schedule_id == ScheduleSchema.id,
            ScheduleSchema.id == self.schedule_id,
        )
        custom_filters.append(schedule_filter)

    if self.build_id:
        pipeline_build_filter = and_(
            PipelineRunSchema.deployment_id == PipelineDeploymentSchema.id,
            PipelineDeploymentSchema.build_id == PipelineBuildSchema.id,
            PipelineBuildSchema.id == self.build_id,
        )
        custom_filters.append(pipeline_build_filter)

    if self.template_id:
        run_template_filter = and_(
            PipelineRunSchema.deployment_id == PipelineDeploymentSchema.id,
            PipelineDeploymentSchema.template_id == self.template_id,
        )
        custom_filters.append(run_template_filter)

    if self.pipeline:
        pipeline_filter = and_(
            PipelineRunSchema.pipeline_id == PipelineSchema.id,
            self.generate_name_or_id_query_conditions(
                value=self.pipeline, table=PipelineSchema
            ),
        )
        custom_filters.append(pipeline_filter)

    if self.stack:
        stack_filter = and_(
            PipelineRunSchema.deployment_id == PipelineDeploymentSchema.id,
            PipelineDeploymentSchema.stack_id == StackSchema.id,
            self.generate_name_or_id_query_conditions(
                value=self.stack,
                table=StackSchema,
            ),
        )
        custom_filters.append(stack_filter)

    if self.code_repository:
        code_repo_filter = and_(
            PipelineRunSchema.deployment_id == PipelineDeploymentSchema.id,
            PipelineDeploymentSchema.code_reference_id
            == CodeReferenceSchema.id,
            CodeReferenceSchema.code_repository_id
            == CodeRepositorySchema.id,
            self.generate_name_or_id_query_conditions(
                value=self.code_repository,
                table=CodeRepositorySchema,
            ),
        )
        custom_filters.append(code_repo_filter)

    if self.model:
        model_filter = and_(
            PipelineRunSchema.model_version_id == ModelVersionSchema.id,
            ModelVersionSchema.model_id == ModelSchema.id,
            self.generate_name_or_id_query_conditions(
                value=self.model, table=ModelSchema
            ),
        )
        custom_filters.append(model_filter)

    if self.stack_component:
        component_filter = and_(
            PipelineRunSchema.deployment_id == PipelineDeploymentSchema.id,
            PipelineDeploymentSchema.stack_id == StackSchema.id,
            StackSchema.id == StackCompositionSchema.stack_id,
            StackCompositionSchema.component_id == StackComponentSchema.id,
            self.generate_name_or_id_query_conditions(
                value=self.stack_component,
                table=StackComponentSchema,
            ),
        )
        custom_filters.append(component_filter)

    if self.pipeline_name:
        pipeline_name_filter = and_(
            PipelineRunSchema.pipeline_id == PipelineSchema.id,
            self.generate_custom_query_conditions_for_column(
                value=self.pipeline_name,
                table=PipelineSchema,
                column="name",
            ),
        )
        custom_filters.append(pipeline_name_filter)

    if self.templatable is not None:
        if self.templatable is True:
            templatable_filter = and_(
                # The following condition is not perfect as it does not
                # consider stacks with custom flavor components or local
                # components, but the best we can do currently with our
                # table columns.
                PipelineRunSchema.deployment_id
                == PipelineDeploymentSchema.id,
                PipelineDeploymentSchema.build_id
                == PipelineBuildSchema.id,
                col(PipelineBuildSchema.is_local).is_(False),
                col(PipelineBuildSchema.stack_id).is_not(None),
            )
        else:
            templatable_filter = or_(
                col(PipelineRunSchema.deployment_id).is_(None),
                and_(
                    PipelineRunSchema.deployment_id
                    == PipelineDeploymentSchema.id,
                    col(PipelineDeploymentSchema.build_id).is_(None),
                ),
                and_(
                    PipelineRunSchema.deployment_id
                    == PipelineDeploymentSchema.id,
                    PipelineDeploymentSchema.build_id
                    == PipelineBuildSchema.id,
                    or_(
                        col(PipelineBuildSchema.is_local).is_(True),
                        col(PipelineBuildSchema.stack_id).is_(None),
                    ),
                ),
            )

        custom_filters.append(templatable_filter)
    if self.run_metadata is not None:
        from zenml.enums import MetadataResourceTypes

        for key, value in self.run_metadata.items():
            additional_filter = and_(
                RunMetadataResourceSchema.resource_id
                == PipelineRunSchema.id,
                RunMetadataResourceSchema.resource_type
                == MetadataResourceTypes.PIPELINE_RUN.value,
                RunMetadataResourceSchema.run_metadata_id
                == RunMetadataSchema.id,
                self.generate_custom_query_conditions_for_column(
                    value=key,
                    table=RunMetadataSchema,
                    column="key",
                ),
                self.generate_custom_query_conditions_for_column(
                    value=value,
                    table=RunMetadataSchema,
                    column="value",
                    json_encode_value=True,
                ),
            )
            custom_filters.append(additional_filter)

    return custom_filters

`PipelineRunRequest`

Request model for pipeline runs.

Source code in src/zenml/models/v2/core/pipeline_run.py

class PipelineRunRequest(WorkspaceScopedRequest):
    """Request model for pipeline runs."""

    name: str = Field(
        title="The name of the pipeline run.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    deployment: UUID = Field(
        title="The deployment associated with the pipeline run."
    )
    pipeline: Optional[UUID] = Field(
        title="The pipeline associated with the pipeline run.",
        default=None,
    )
    orchestrator_run_id: Optional[str] = Field(
        title="The orchestrator run ID.",
        max_length=STR_FIELD_MAX_LENGTH,
        default=None,
    )
    start_time: Optional[datetime] = Field(
        title="The start time of the pipeline run.",
        default=None,
    )
    end_time: Optional[datetime] = Field(
        title="The end time of the pipeline run.",
        default=None,
    )
    status: ExecutionStatus = Field(
        title="The status of the pipeline run.",
    )
    client_environment: Dict[str, str] = Field(
        default={},
        title=(
            "Environment of the client that initiated this pipeline run "
            "(OS, Python version, etc.)."
        ),
    )
    orchestrator_environment: Dict[str, str] = Field(
        default={},
        title=(
            "Environment of the orchestrator that executed this pipeline run "
            "(OS, Python version, etc.)."
        ),
    )
    trigger_execution_id: Optional[UUID] = Field(
        default=None,
        title="ID of the trigger execution that triggered this run.",
    )
    tags: Optional[List[str]] = Field(
        default=None,
        title="Tags of the pipeline run.",
    )
    model_version_id: Optional[UUID] = Field(
        title="The ID of the model version that was "
        "configured by this pipeline run explicitly.",
        default=None,
    )

    model_config = ConfigDict(protected_namespaces=())

`PipelineRunResponse`

Bases: WorkspaceScopedResponse[PipelineRunResponseBody, PipelineRunResponseMetadata, PipelineRunResponseResources]

Response model for pipeline runs.

Source code in src/zenml/models/v2/core/pipeline_run.py

class PipelineRunResponse(
    WorkspaceScopedResponse[
        PipelineRunResponseBody,
        PipelineRunResponseMetadata,
        PipelineRunResponseResources,
    ]
):
    """Response model for pipeline runs."""

    name: str = Field(
        title="The name of the pipeline run.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    def get_hydrated_version(self) -> "PipelineRunResponse":
        """Get the hydrated version of this pipeline run.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_run(self.id)

    # Helper methods
    @property
    def artifact_versions(self) -> List["ArtifactVersionResponse"]:
        """Get all artifact versions that are outputs of steps of this run.

        Returns:
            All output artifact versions of this run (including cached ones).
        """
        from zenml.artifacts.utils import (
            get_artifacts_versions_of_pipeline_run,
        )

        return get_artifacts_versions_of_pipeline_run(self)

    @property
    def produced_artifact_versions(self) -> List["ArtifactVersionResponse"]:
        """Get all artifact versions produced during this pipeline run.

        Returns:
            A list of all artifact versions produced during this pipeline run.
        """
        from zenml.artifacts.utils import (
            get_artifacts_versions_of_pipeline_run,
        )

        return get_artifacts_versions_of_pipeline_run(self, only_produced=True)

    def refresh_run_status(self) -> "PipelineRunResponse":
        """Method to refresh the status of a run if it is initializing/running.

        Returns:
            The updated pipeline.

        Raises:
            ValueError: If the stack of the run response is None.
        """
        if self.status in [
            ExecutionStatus.INITIALIZING,
            ExecutionStatus.RUNNING,
        ]:
            # Check if the stack still accessible
            if self.stack is None:
                raise ValueError(
                    "The stack that this pipeline run response was executed on"
                    "has been deleted."
                )

            # Create the orchestrator instance
            from zenml.enums import StackComponentType
            from zenml.orchestrators.base_orchestrator import BaseOrchestrator
            from zenml.stack.stack_component import StackComponent

            # Check if the stack still accessible
            orchestrator_list = self.stack.components.get(
                StackComponentType.ORCHESTRATOR, []
            )
            if len(orchestrator_list) == 0:
                raise ValueError(
                    "The orchestrator that this pipeline run response was "
                    "executed with has been deleted."
                )

            orchestrator = cast(
                BaseOrchestrator,
                StackComponent.from_model(
                    component_model=orchestrator_list[0]
                ),
            )

            # Fetch the status
            status = orchestrator.fetch_status(run=self)

            # If it is different from the current status, update it
            if status != self.status:
                from zenml.client import Client
                from zenml.models import PipelineRunUpdate

                client = Client()
                return client.zen_store.update_run(
                    run_id=self.id,
                    run_update=PipelineRunUpdate(status=status),
                )

        return self

    # Body and metadata properties
    @property
    def status(self) -> ExecutionStatus:
        """The `status` property.

        Returns:
            the value of the property.
        """
        return self.get_body().status

    @property
    def stack(self) -> Optional["StackResponse"]:
        """The `stack` property.

        Returns:
            the value of the property.
        """
        return self.get_body().stack

    @property
    def pipeline(self) -> Optional["PipelineResponse"]:
        """The `pipeline` property.

        Returns:
            the value of the property.
        """
        return self.get_body().pipeline

    @property
    def build(self) -> Optional["PipelineBuildResponse"]:
        """The `build` property.

        Returns:
            the value of the property.
        """
        return self.get_body().build

    @property
    def schedule(self) -> Optional["ScheduleResponse"]:
        """The `schedule` property.

        Returns:
            the value of the property.
        """
        return self.get_body().schedule

    @property
    def trigger_execution(self) -> Optional["TriggerExecutionResponse"]:
        """The `trigger_execution` property.

        Returns:
            the value of the property.
        """
        return self.get_body().trigger_execution

    @property
    def code_reference(self) -> Optional["CodeReferenceResponse"]:
        """The `schedule` property.

        Returns:
            the value of the property.
        """
        return self.get_body().code_reference

    @property
    def deployment_id(self) -> Optional["UUID"]:
        """The `deployment_id` property.

        Returns:
            the value of the property.
        """
        return self.get_body().deployment_id

    @property
    def model_version_id(self) -> Optional[UUID]:
        """The `model_version_id` property.

        Returns:
            the value of the property.
        """
        return self.get_body().model_version_id

    @property
    def run_metadata(self) -> Dict[str, MetadataType]:
        """The `run_metadata` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().run_metadata

    @property
    def steps(self) -> Dict[str, "StepRunResponse"]:
        """The `steps` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().steps

    @property
    def config(self) -> PipelineConfiguration:
        """The `config` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().config

    @property
    def start_time(self) -> Optional[datetime]:
        """The `start_time` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().start_time

    @property
    def end_time(self) -> Optional[datetime]:
        """The `end_time` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().end_time

    @property
    def client_environment(self) -> Dict[str, str]:
        """The `client_environment` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().client_environment

    @property
    def orchestrator_environment(self) -> Dict[str, str]:
        """The `orchestrator_environment` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().orchestrator_environment

    @property
    def orchestrator_run_id(self) -> Optional[str]:
        """The `orchestrator_run_id` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().orchestrator_run_id

    @property
    def code_path(self) -> Optional[str]:
        """The `code_path` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().code_path

    @property
    def template_id(self) -> Optional[UUID]:
        """The `template_id` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().template_id

    @property
    def is_templatable(self) -> bool:
        """The `is_templatable` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().is_templatable

    @property
    def step_substitutions(self) -> Dict[str, Dict[str, str]]:
        """The `step_substitutions` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().step_substitutions

    @property
    def model_version(self) -> Optional[ModelVersionResponse]:
        """The `model_version` property.

        Returns:
            the value of the property.
        """
        return self.get_resources().model_version

    @property
    def tags(self) -> List[TagResponse]:
        """The `tags` property.

        Returns:
            the value of the property.
        """
        return self.get_resources().tags

`artifact_versions` `property`

Get all artifact versions that are outputs of steps of this run.

Returns:

Type	Description
`List[ArtifactVersionResponse]`	All output artifact versions of this run (including cached ones).

`build` `property`

The build property.

Returns:

Type	Description
`Optional[PipelineBuildResponse]`	the value of the property.

`client_environment` `property`

The client_environment property.

Returns:

Type	Description
`Dict[str, str]`	the value of the property.

`code_path` `property`

The code_path property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`code_reference` `property`

The schedule property.

Returns:

Type	Description
`Optional[CodeReferenceResponse]`	the value of the property.

`config` `property`

The config property.

Returns:

Type	Description
`PipelineConfiguration`	the value of the property.

`deployment_id` `property`

The deployment_id property.

Returns:

Type	Description
`Optional[UUID]`	the value of the property.

`end_time` `property`

The end_time property.

Returns:

Type	Description
`Optional[datetime]`	the value of the property.

`is_templatable` `property`

The is_templatable property.

Returns:

Type	Description
`bool`	the value of the property.

`model_version` `property`

The model_version property.

Returns:

Type	Description
`Optional[ModelVersionResponse]`	the value of the property.

`model_version_id` `property`

The model_version_id property.

Returns:

Type	Description
`Optional[UUID]`	the value of the property.

`orchestrator_environment` `property`

The orchestrator_environment property.

Returns:

Type	Description
`Dict[str, str]`	the value of the property.

`orchestrator_run_id` `property`

The orchestrator_run_id property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`pipeline` `property`

The pipeline property.

Returns:

Type	Description
`Optional[PipelineResponse]`	the value of the property.

`produced_artifact_versions` `property`

Get all artifact versions produced during this pipeline run.

Returns:

Type	Description
`List[ArtifactVersionResponse]`	A list of all artifact versions produced during this pipeline run.

`run_metadata` `property`

The run_metadata property.

Returns:

Type	Description
`Dict[str, MetadataType]`	the value of the property.

`schedule` `property`

The schedule property.

Returns:

Type	Description
`Optional[ScheduleResponse]`	the value of the property.

`stack` `property`

The stack property.

Returns:

Type	Description
`Optional[StackResponse]`	the value of the property.

`start_time` `property`

The start_time property.

Returns:

Type	Description
`Optional[datetime]`	the value of the property.

`status` `property`

The status property.

Returns:

Type	Description
`ExecutionStatus`	the value of the property.

`step_substitutions` `property`

The step_substitutions property.

Returns:

Type	Description
`Dict[str, Dict[str, str]]`	the value of the property.

`steps` `property`

The steps property.

Returns:

Type	Description
`Dict[str, StepRunResponse]`	the value of the property.

`tags` `property`

The tags property.

Returns:

Type	Description
`List[TagResponse]`	the value of the property.

`template_id` `property`

The template_id property.

Returns:

Type	Description
`Optional[UUID]`	the value of the property.

`trigger_execution` `property`

The trigger_execution property.

Returns:

Type	Description
`Optional[TriggerExecutionResponse]`	the value of the property.

`get_hydrated_version()`

Get the hydrated version of this pipeline run.

Returns:

Type	Description
`PipelineRunResponse`	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/pipeline_run.py

def get_hydrated_version(self) -> "PipelineRunResponse":
    """Get the hydrated version of this pipeline run.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_run(self.id)

`refresh_run_status()`

Method to refresh the status of a run if it is initializing/running.

Returns:

Type	Description
`PipelineRunResponse`	The updated pipeline.

Raises:

Type	Description
`ValueError`	If the stack of the run response is None.

Source code in src/zenml/models/v2/core/pipeline_run.py

def refresh_run_status(self) -> "PipelineRunResponse":
    """Method to refresh the status of a run if it is initializing/running.

    Returns:
        The updated pipeline.

    Raises:
        ValueError: If the stack of the run response is None.
    """
    if self.status in [
        ExecutionStatus.INITIALIZING,
        ExecutionStatus.RUNNING,
    ]:
        # Check if the stack still accessible
        if self.stack is None:
            raise ValueError(
                "The stack that this pipeline run response was executed on"
                "has been deleted."
            )

        # Create the orchestrator instance
        from zenml.enums import StackComponentType
        from zenml.orchestrators.base_orchestrator import BaseOrchestrator
        from zenml.stack.stack_component import StackComponent

        # Check if the stack still accessible
        orchestrator_list = self.stack.components.get(
            StackComponentType.ORCHESTRATOR, []
        )
        if len(orchestrator_list) == 0:
            raise ValueError(
                "The orchestrator that this pipeline run response was "
                "executed with has been deleted."
            )

        orchestrator = cast(
            BaseOrchestrator,
            StackComponent.from_model(
                component_model=orchestrator_list[0]
            ),
        )

        # Fetch the status
        status = orchestrator.fetch_status(run=self)

        # If it is different from the current status, update it
        if status != self.status:
            from zenml.client import Client
            from zenml.models import PipelineRunUpdate

            client = Client()
            return client.zen_store.update_run(
                run_id=self.id,
                run_update=PipelineRunUpdate(status=status),
            )

    return self

`PipelineRunResponseBody`

Response body for pipeline runs.

Source code in src/zenml/models/v2/core/pipeline_run.py

class PipelineRunResponseBody(WorkspaceScopedResponseBody):
    """Response body for pipeline runs."""

    status: ExecutionStatus = Field(
        title="The status of the pipeline run.",
    )
    stack: Optional["StackResponse"] = Field(
        default=None, title="The stack that was used for this run."
    )
    pipeline: Optional["PipelineResponse"] = Field(
        default=None, title="The pipeline this run belongs to."
    )
    build: Optional["PipelineBuildResponse"] = Field(
        default=None, title="The pipeline build that was used for this run."
    )
    schedule: Optional["ScheduleResponse"] = Field(
        default=None, title="The schedule that was used for this run."
    )
    code_reference: Optional["CodeReferenceResponse"] = Field(
        default=None, title="The code reference that was used for this run."
    )
    deployment_id: Optional[UUID] = Field(
        default=None, title="The deployment that was used for this run."
    )
    trigger_execution: Optional["TriggerExecutionResponse"] = Field(
        default=None, title="The trigger execution that triggered this run."
    )
    model_version_id: Optional[UUID] = Field(
        title="The ID of the model version that was "
        "configured by this pipeline run explicitly.",
        default=None,
    )

    model_config = ConfigDict(protected_namespaces=())

`PipelineRunResponseMetadata`

Response metadata for pipeline runs.

Source code in src/zenml/models/v2/core/pipeline_run.py

class PipelineRunResponseMetadata(WorkspaceScopedResponseMetadata):
    """Response metadata for pipeline runs."""

    run_metadata: Dict[str, MetadataType] = Field(
        default={},
        title="Metadata associated with this pipeline run.",
    )
    steps: Dict[str, "StepRunResponse"] = Field(
        default={}, title="The steps of this run."
    )
    config: PipelineConfiguration = Field(
        title="The pipeline configuration used for this pipeline run.",
    )
    start_time: Optional[datetime] = Field(
        title="The start time of the pipeline run.",
        default=None,
    )
    end_time: Optional[datetime] = Field(
        title="The end time of the pipeline run.",
        default=None,
    )
    client_environment: Dict[str, str] = Field(
        default={},
        title=(
            "Environment of the client that initiated this pipeline run "
            "(OS, Python version, etc.)."
        ),
    )
    orchestrator_environment: Dict[str, str] = Field(
        default={},
        title=(
            "Environment of the orchestrator that executed this pipeline run "
            "(OS, Python version, etc.)."
        ),
    )
    orchestrator_run_id: Optional[str] = Field(
        title="The orchestrator run ID.",
        max_length=STR_FIELD_MAX_LENGTH,
        default=None,
    )
    code_path: Optional[str] = Field(
        default=None,
        title="Optional path where the code is stored in the artifact store.",
    )
    template_id: Optional[UUID] = Field(
        default=None,
        description="Template used for the pipeline run.",
    )
    is_templatable: bool = Field(
        default=False,
        description="Whether a template can be created from this run.",
    )
    step_substitutions: Dict[str, Dict[str, str]] = Field(
        title="Substitutions used in the step runs of this pipeline run.",
        default_factory=dict,
    )

`PipelineRunResponseResources`

Class for all resource models associated with the pipeline run entity.

Source code in src/zenml/models/v2/core/pipeline_run.py

class PipelineRunResponseResources(WorkspaceScopedResponseResources):
    """Class for all resource models associated with the pipeline run entity."""

    model_version: Optional[ModelVersionResponse] = None
    tags: List[TagResponse] = Field(
        title="Tags associated with the pipeline run.",
    )

    # TODO: In Pydantic v2, the `model_` is a protected namespaces for all
    #  fields defined under base models. If not handled, this raises a warning.
    #  It is possible to suppress this warning message with the following
    #  configuration, however the ultimate solution is to rename these fields.
    #  Even though they do not cause any problems right now, if we are not
    #  careful we might overwrite some fields protected by pydantic.
    model_config = ConfigDict(protected_namespaces=())

`PipelineRunUpdate`

Bases: BaseModel

Pipeline run update model.

Source code in src/zenml/models/v2/core/pipeline_run.py

class PipelineRunUpdate(BaseModel):
    """Pipeline run update model."""

    status: Optional[ExecutionStatus] = None
    end_time: Optional[datetime] = None
    model_version_id: Optional[UUID] = Field(
        title="The ID of the model version that was "
        "configured by this pipeline run explicitly.",
        default=None,
    )
    # TODO: we should maybe have a different update model here, the upper
    #  three attributes should only be for internal use
    add_tags: Optional[List[str]] = Field(
        default=None, title="New tags to add to the pipeline run."
    )
    remove_tags: Optional[List[str]] = Field(
        default=None, title="Tags to remove from the pipeline run."
    )

    model_config = ConfigDict(protected_namespaces=())

`PipelineUpdate`

Bases: BaseUpdate

Update model for pipelines.

Source code in src/zenml/models/v2/core/pipeline.py

class PipelineUpdate(BaseUpdate):
    """Update model for pipelines."""

    description: Optional[str] = Field(
        default=None,
        title="The description of the pipeline.",
        max_length=TEXT_FIELD_MAX_LENGTH,
    )
    add_tags: Optional[List[str]] = Field(
        default=None, title="New tags to add to the pipeline."
    )
    remove_tags: Optional[List[str]] = Field(
        default=None, title="Tags to remove from the pipeline."
    )

`ResourceTypeModel`

Bases: BaseModel

Resource type specification.

Describes the authentication methods and resource instantiation model for one or more resource types.

Source code in src/zenml/models/v2/misc/service_connector_type.py

class ResourceTypeModel(BaseModel):
    """Resource type specification.

    Describes the authentication methods and resource instantiation model for
    one or more resource types.
    """

    name: str = Field(
        title="User readable name for the resource type.",
    )
    resource_type: str = Field(
        title="Resource type identifier.",
    )
    description: str = Field(
        default="",
        title="A description of the resource type.",
    )
    auth_methods: List[str] = Field(
        title="The list of authentication methods that can be used to access "
        "resources of this type.",
    )
    supports_instances: bool = Field(
        default=False,
        title="Specifies if a single connector instance can be used to access "
        "multiple instances of this resource type. If set to True, the "
        "connector is able to provide a list of resource IDs identifying all "
        "the resources that it can access and a resource ID needs to be "
        "explicitly configured or supplied when access to a resource is "
        "requested. If set to False, a connector instance is only able to "
        "access a single resource and a resource ID is not required to access "
        "the resource.",
    )
    logo_url: Optional[str] = Field(
        default=None,
        title="Optionally, a URL pointing to a png,"
        "svg or jpg file can be attached.",
    )
    emoji: Optional[str] = Field(
        default=None,
        title="Optionally, a python-rich emoji can be attached.",
    )

    @property
    def emojified_resource_type(self) -> str:
        """Get the emojified resource type.

        Returns:
            The emojified resource type.
        """
        if not self.emoji:
            return self.resource_type
        return f"{self.emoji} {self.resource_type}"

`emojified_resource_type` `property`

Get the emojified resource type.

Returns:

Type	Description
`str`	The emojified resource type.

`ResourcesInfo`

Bases: BaseModel

Information about the resources needed for CLI and UI.

Source code in src/zenml/models/v2/misc/info_models.py

class ResourcesInfo(BaseModel):
    """Information about the resources needed for CLI and UI."""

    flavor: str
    flavor_display_name: str
    required_configuration: Dict[str, str] = {}
    use_resource_value_as_fixed_config: bool = False

    accessible_by_service_connector: List[str]
    connected_through_service_connector: List["ComponentResponse"]

    @model_validator(mode="after")
    def _validate_resource_info(self) -> "ResourcesInfo":
        if (
            self.use_resource_value_as_fixed_config
            and len(self.required_configuration) > 1
        ):
            raise ValueError(
                "Cannot use resource value as fixed config if more than "
                "one required configuration key is provided."
            )
        return self

`RunMetadataEntry`

Bases: BaseModel

Utility class to sort/list run metadata entries.

Source code in src/zenml/models/v2/misc/run_metadata.py

class RunMetadataEntry(BaseModel):
    """Utility class to sort/list run metadata entries."""

    value: MetadataType = Field(title="The value for the run metadata entry")
    created: datetime = Field(
        title="The timestamp when this resource was created."
    )

`RunMetadataRequest`

Request model for run metadata.

Source code in src/zenml/models/v2/core/run_metadata.py

class RunMetadataRequest(WorkspaceScopedRequest):
    """Request model for run metadata."""

    resources: List[RunMetadataResource] = Field(
        title="The list of resources that this metadata belongs to."
    )
    stack_component_id: Optional[UUID] = Field(
        title="The ID of the stack component that this metadata belongs to.",
        default=None,
    )
    values: Dict[str, "MetadataType"] = Field(
        title="The metadata to be created.",
    )
    types: Dict[str, "MetadataTypeEnum"] = Field(
        title="The types of the metadata to be created.",
    )
    publisher_step_id: Optional[UUID] = Field(
        title="The ID of the step execution that published this metadata.",
        default=None,
    )

    @model_validator(mode="after")
    def validate_values_keys(self) -> "RunMetadataRequest":
        """Validates if the keys in the metadata are properly defined.

        Returns:
            self

        Raises:
            ValueError: if one of the key in the metadata contains `:`
        """
        invalid_keys = [key for key in self.values.keys() if ":" in key]
        if invalid_keys:
            raise ValueError(
                "You can not use colons (`:`) in the key names when you "
                "are creating metadata for your ZenML objects. Please change "
                f"the following keys: {invalid_keys}"
            )
        return self

`validate_values_keys()`

Validates if the keys in the metadata are properly defined.

Returns:

Type	Description
`RunMetadataRequest`	self

Raises:

Type	Description
`ValueError`	if one of the key in the metadata contains `:`

Source code in src/zenml/models/v2/core/run_metadata.py

@model_validator(mode="after")
def validate_values_keys(self) -> "RunMetadataRequest":
    """Validates if the keys in the metadata are properly defined.

    Returns:
        self

    Raises:
        ValueError: if one of the key in the metadata contains `:`
    """
    invalid_keys = [key for key in self.values.keys() if ":" in key]
    if invalid_keys:
        raise ValueError(
            "You can not use colons (`:`) in the key names when you "
            "are creating metadata for your ZenML objects. Please change "
            f"the following keys: {invalid_keys}"
        )
    return self

`RunMetadataResource`

Bases: BaseModel

Utility class to help identify resources to tag metadata to.

Source code in src/zenml/models/v2/misc/run_metadata.py

class RunMetadataResource(BaseModel):
    """Utility class to help identify resources to tag metadata to."""

    id: UUID = Field(title="The ID of the resource.")
    type: MetadataResourceTypes = Field(title="The type of the resource.")

`RunTemplateFilter`

Model for filtering of run templates.

Source code in src/zenml/models/v2/core/run_template.py

class RunTemplateFilter(WorkspaceScopedFilter, TaggableFilter):
    """Model for filtering of run templates."""

    FILTER_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        *WorkspaceScopedFilter.FILTER_EXCLUDE_FIELDS,
        *TaggableFilter.FILTER_EXCLUDE_FIELDS,
        "code_repository_id",
        "stack_id",
        "build_id",
        "pipeline_id",
        "user",
        "pipeline",
        "stack",
    ]
    CUSTOM_SORTING_OPTIONS = [
        *WorkspaceScopedFilter.CUSTOM_SORTING_OPTIONS,
        *TaggableFilter.CUSTOM_SORTING_OPTIONS,
    ]
    CLI_EXCLUDE_FIELDS = [
        *WorkspaceScopedFilter.CLI_EXCLUDE_FIELDS,
        *TaggableFilter.CLI_EXCLUDE_FIELDS,
    ]

    name: Optional[str] = Field(
        default=None,
        description="Name of the run template.",
    )
    pipeline_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Pipeline associated with the template.",
        union_mode="left_to_right",
    )
    build_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Build associated with the template.",
        union_mode="left_to_right",
    )
    stack_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Stack associated with the template.",
        union_mode="left_to_right",
    )
    code_repository_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Code repository associated with the template.",
        union_mode="left_to_right",
    )
    pipeline: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Name/ID of the pipeline associated with the template.",
    )
    stack: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Name/ID of the stack associated with the template.",
    )

    def get_custom_filters(
        self, table: Type["AnySchema"]
    ) -> List["ColumnElement[bool]"]:
        """Get custom filters.

        Args:
            table: The query table.

        Returns:
            A list of custom filters.
        """
        custom_filters = super().get_custom_filters(table)

        from sqlmodel import and_

        from zenml.zen_stores.schemas import (
            CodeReferenceSchema,
            PipelineDeploymentSchema,
            PipelineSchema,
            RunTemplateSchema,
            StackSchema,
        )

        if self.code_repository_id:
            code_repo_filter = and_(
                RunTemplateSchema.source_deployment_id
                == PipelineDeploymentSchema.id,
                PipelineDeploymentSchema.code_reference_id
                == CodeReferenceSchema.id,
                CodeReferenceSchema.code_repository_id
                == self.code_repository_id,
            )
            custom_filters.append(code_repo_filter)

        if self.stack_id:
            stack_filter = and_(
                RunTemplateSchema.source_deployment_id
                == PipelineDeploymentSchema.id,
                PipelineDeploymentSchema.stack_id == self.stack_id,
            )
            custom_filters.append(stack_filter)

        if self.build_id:
            build_filter = and_(
                RunTemplateSchema.source_deployment_id
                == PipelineDeploymentSchema.id,
                PipelineDeploymentSchema.build_id == self.build_id,
            )
            custom_filters.append(build_filter)

        if self.pipeline_id:
            pipeline_filter = and_(
                RunTemplateSchema.source_deployment_id
                == PipelineDeploymentSchema.id,
                PipelineDeploymentSchema.pipeline_id == self.pipeline_id,
            )
            custom_filters.append(pipeline_filter)

        if self.pipeline:
            pipeline_filter = and_(
                RunTemplateSchema.source_deployment_id
                == PipelineDeploymentSchema.id,
                PipelineDeploymentSchema.pipeline_id == PipelineSchema.id,
                self.generate_name_or_id_query_conditions(
                    value=self.pipeline,
                    table=PipelineSchema,
                ),
            )
            custom_filters.append(pipeline_filter)

        if self.stack:
            stack_filter = and_(
                RunTemplateSchema.source_deployment_id
                == PipelineDeploymentSchema.id,
                PipelineDeploymentSchema.stack_id == StackSchema.id,
                self.generate_name_or_id_query_conditions(
                    value=self.stack,
                    table=StackSchema,
                ),
            )
            custom_filters.append(stack_filter)

        return custom_filters

`get_custom_filters(table)`

Get custom filters.

Parameters:

Name	Type	Description	Default
`table`	`Type[AnySchema]`	The query table.	required

Returns:

Type	Description
`List[ColumnElement[bool]]`	A list of custom filters.

Source code in src/zenml/models/v2/core/run_template.py

def get_custom_filters(
    self, table: Type["AnySchema"]
) -> List["ColumnElement[bool]"]:
    """Get custom filters.

    Args:
        table: The query table.

    Returns:
        A list of custom filters.
    """
    custom_filters = super().get_custom_filters(table)

    from sqlmodel import and_

    from zenml.zen_stores.schemas import (
        CodeReferenceSchema,
        PipelineDeploymentSchema,
        PipelineSchema,
        RunTemplateSchema,
        StackSchema,
    )

    if self.code_repository_id:
        code_repo_filter = and_(
            RunTemplateSchema.source_deployment_id
            == PipelineDeploymentSchema.id,
            PipelineDeploymentSchema.code_reference_id
            == CodeReferenceSchema.id,
            CodeReferenceSchema.code_repository_id
            == self.code_repository_id,
        )
        custom_filters.append(code_repo_filter)

    if self.stack_id:
        stack_filter = and_(
            RunTemplateSchema.source_deployment_id
            == PipelineDeploymentSchema.id,
            PipelineDeploymentSchema.stack_id == self.stack_id,
        )
        custom_filters.append(stack_filter)

    if self.build_id:
        build_filter = and_(
            RunTemplateSchema.source_deployment_id
            == PipelineDeploymentSchema.id,
            PipelineDeploymentSchema.build_id == self.build_id,
        )
        custom_filters.append(build_filter)

    if self.pipeline_id:
        pipeline_filter = and_(
            RunTemplateSchema.source_deployment_id
            == PipelineDeploymentSchema.id,
            PipelineDeploymentSchema.pipeline_id == self.pipeline_id,
        )
        custom_filters.append(pipeline_filter)

    if self.pipeline:
        pipeline_filter = and_(
            RunTemplateSchema.source_deployment_id
            == PipelineDeploymentSchema.id,
            PipelineDeploymentSchema.pipeline_id == PipelineSchema.id,
            self.generate_name_or_id_query_conditions(
                value=self.pipeline,
                table=PipelineSchema,
            ),
        )
        custom_filters.append(pipeline_filter)

    if self.stack:
        stack_filter = and_(
            RunTemplateSchema.source_deployment_id
            == PipelineDeploymentSchema.id,
            PipelineDeploymentSchema.stack_id == StackSchema.id,
            self.generate_name_or_id_query_conditions(
                value=self.stack,
                table=StackSchema,
            ),
        )
        custom_filters.append(stack_filter)

    return custom_filters

`RunTemplateRequest`

Request model for run templates.

Source code in src/zenml/models/v2/core/run_template.py

class RunTemplateRequest(WorkspaceScopedRequest):
    """Request model for run templates."""

    name: str = Field(
        title="The name of the run template.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    description: Optional[str] = Field(
        default=None,
        title="The description of the run template.",
        max_length=TEXT_FIELD_MAX_LENGTH,
    )
    source_deployment_id: UUID = Field(
        title="The deployment that should be the base of the created template."
    )
    tags: Optional[List[str]] = Field(
        default=None,
        title="Tags of the run template.",
    )

`RunTemplateResponse`

Bases: WorkspaceScopedResponse[RunTemplateResponseBody, RunTemplateResponseMetadata, RunTemplateResponseResources]

Response model for run templates.

Source code in src/zenml/models/v2/core/run_template.py

class RunTemplateResponse(
    WorkspaceScopedResponse[
        RunTemplateResponseBody,
        RunTemplateResponseMetadata,
        RunTemplateResponseResources,
    ]
):
    """Response model for run templates."""

    name: str = Field(
        title="The name of the run template.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    def get_hydrated_version(self) -> "RunTemplateResponse":
        """Return the hydrated version of this run template.

        Returns:
            The hydrated run template.
        """
        from zenml.client import Client

        return Client().zen_store.get_run_template(
            template_id=self.id, hydrate=True
        )

    # Body and metadata properties
    @property
    def runnable(self) -> bool:
        """The `runnable` property.

        Returns:
            the value of the property.
        """
        return self.get_body().runnable

    @property
    def latest_run_id(self) -> Optional[UUID]:
        """The `latest_run_id` property.

        Returns:
            the value of the property.
        """
        return self.get_body().latest_run_id

    @property
    def latest_run_status(self) -> Optional[ExecutionStatus]:
        """The `latest_run_status` property.

        Returns:
            the value of the property.
        """
        return self.get_body().latest_run_status

    @property
    def description(self) -> Optional[str]:
        """The `description` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().description

    @property
    def pipeline_spec(self) -> Optional[PipelineSpec]:
        """The `pipeline_spec` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().pipeline_spec

    @property
    def config_template(self) -> Optional[Dict[str, Any]]:
        """The `config_template` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().config_template

    @property
    def config_schema(self) -> Optional[Dict[str, Any]]:
        """The `config_schema` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().config_schema

    @property
    def source_deployment(self) -> Optional[PipelineDeploymentResponse]:
        """The `source_deployment` property.

        Returns:
            the value of the property.
        """
        return self.get_resources().source_deployment

    @property
    def pipeline(self) -> Optional[PipelineResponse]:
        """The `pipeline` property.

        Returns:
            the value of the property.
        """
        return self.get_resources().pipeline

    @property
    def build(self) -> Optional[PipelineBuildResponse]:
        """The `build` property.

        Returns:
            the value of the property.
        """
        return self.get_resources().build

    @property
    def code_reference(self) -> Optional[CodeReferenceResponse]:
        """The `code_reference` property.

        Returns:
            the value of the property.
        """
        return self.get_resources().code_reference

    @property
    def tags(self) -> List[TagResponse]:
        """The `tags` property.

        Returns:
            the value of the property.
        """
        return self.get_resources().tags

`build` `property`

The build property.

Returns:

Type	Description
`Optional[PipelineBuildResponse]`	the value of the property.

`code_reference` `property`

The code_reference property.

Returns:

Type	Description
`Optional[CodeReferenceResponse]`	the value of the property.

`config_schema` `property`

The config_schema property.

Returns:

Type	Description
`Optional[Dict[str, Any]]`	the value of the property.

`config_template` `property`

The config_template property.

Returns:

Type	Description
`Optional[Dict[str, Any]]`	the value of the property.

`description` `property`

The description property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`latest_run_id` `property`

The latest_run_id property.

Returns:

Type	Description
`Optional[UUID]`	the value of the property.

`latest_run_status` `property`

The latest_run_status property.

Returns:

Type	Description
`Optional[ExecutionStatus]`	the value of the property.

`pipeline` `property`

The pipeline property.

Returns:

Type	Description
`Optional[PipelineResponse]`	the value of the property.

`pipeline_spec` `property`

The pipeline_spec property.

Returns:

Type	Description
`Optional[PipelineSpec]`	the value of the property.

`runnable` `property`

The runnable property.

Returns:

Type	Description
`bool`	the value of the property.

`source_deployment` `property`

The source_deployment property.

Returns:

Type	Description
`Optional[PipelineDeploymentResponse]`	the value of the property.

`tags` `property`

The tags property.

Returns:

Type	Description
`List[TagResponse]`	the value of the property.

`get_hydrated_version()`

Return the hydrated version of this run template.

Returns:

Type	Description
`RunTemplateResponse`	The hydrated run template.

Source code in src/zenml/models/v2/core/run_template.py

def get_hydrated_version(self) -> "RunTemplateResponse":
    """Return the hydrated version of this run template.

    Returns:
        The hydrated run template.
    """
    from zenml.client import Client

    return Client().zen_store.get_run_template(
        template_id=self.id, hydrate=True
    )

`RunTemplateResponseBody`

Response body for run templates.

Source code in src/zenml/models/v2/core/run_template.py

class RunTemplateResponseBody(WorkspaceScopedResponseBody):
    """Response body for run templates."""

    runnable: bool = Field(
        title="If a run can be started from the template.",
    )
    latest_run_id: Optional[UUID] = Field(
        default=None,
        title="The ID of the latest run of the run template.",
    )
    latest_run_status: Optional[ExecutionStatus] = Field(
        default=None,
        title="The status of the latest run of the run template.",
    )

`RunTemplateResponseMetadata`

Response metadata for run templates.

Source code in src/zenml/models/v2/core/run_template.py

class RunTemplateResponseMetadata(WorkspaceScopedResponseMetadata):
    """Response metadata for run templates."""

    description: Optional[str] = Field(
        default=None,
        title="The description of the run template.",
    )
    pipeline_spec: Optional[PipelineSpec] = Field(
        default=None, title="The spec of the pipeline."
    )
    config_template: Optional[Dict[str, Any]] = Field(
        default=None, title="Run configuration template."
    )
    config_schema: Optional[Dict[str, Any]] = Field(
        default=None, title="Run configuration schema."
    )

`RunTemplateResponseResources`

All resource models associated with the run template.

Source code in src/zenml/models/v2/core/run_template.py

class RunTemplateResponseResources(WorkspaceScopedResponseResources):
    """All resource models associated with the run template."""

    source_deployment: Optional[PipelineDeploymentResponse] = Field(
        default=None,
        title="The deployment that is the source of the template.",
    )
    pipeline: Optional[PipelineResponse] = Field(
        default=None, title="The pipeline associated with the template."
    )
    build: Optional[PipelineBuildResponse] = Field(
        default=None,
        title="The pipeline build associated with the template.",
    )
    code_reference: Optional[CodeReferenceResponse] = Field(
        default=None,
        title="The code reference associated with the template.",
    )
    tags: List[TagResponse] = Field(
        title="Tags associated with the run template.",
    )

`RunTemplateUpdate`

Bases: BaseUpdate

Run template update model.

Source code in src/zenml/models/v2/core/run_template.py

class RunTemplateUpdate(BaseUpdate):
    """Run template update model."""

    name: Optional[str] = Field(
        default=None,
        title="The name of the run template.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    description: Optional[str] = Field(
        default=None,
        title="The description of the run template.",
        max_length=TEXT_FIELD_MAX_LENGTH,
    )
    add_tags: Optional[List[str]] = Field(
        default=None, title="New tags to add to the run template."
    )
    remove_tags: Optional[List[str]] = Field(
        default=None, title="Tags to remove from the run template."
    )

`ScheduleFilter`

Model to enable advanced filtering of all Users.

Source code in src/zenml/models/v2/core/schedule.py

class ScheduleFilter(WorkspaceScopedFilter):
    """Model to enable advanced filtering of all Users."""

    pipeline_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Pipeline that the schedule is attached to.",
        union_mode="left_to_right",
    )
    orchestrator_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Orchestrator that the schedule is attached to.",
        union_mode="left_to_right",
    )
    active: Optional[bool] = Field(
        default=None,
        description="If the schedule is active",
    )
    cron_expression: Optional[str] = Field(
        default=None,
        description="The cron expression, describing the schedule",
    )
    start_time: Optional[Union[datetime, str]] = Field(
        default=None, description="Start time", union_mode="left_to_right"
    )
    end_time: Optional[Union[datetime, str]] = Field(
        default=None, description="End time", union_mode="left_to_right"
    )
    interval_second: Optional[Optional[float]] = Field(
        default=None,
        description="The repetition interval in seconds",
    )
    catchup: Optional[bool] = Field(
        default=None,
        description="Whether or not the schedule is set to catchup past missed "
        "events",
    )
    name: Optional[str] = Field(
        default=None,
        description="Name of the schedule",
    )
    run_once_start_time: Optional[Union[datetime, str]] = Field(
        default=None,
        description="The time at which the schedule should run once",
        union_mode="left_to_right",
    )

`ScheduleRequest`

Request model for schedules.

Source code in src/zenml/models/v2/core/schedule.py

class ScheduleRequest(WorkspaceScopedRequest):
    """Request model for schedules."""

    name: str
    active: bool

    cron_expression: Optional[str] = None
    start_time: Optional[datetime] = None
    end_time: Optional[datetime] = None
    interval_second: Optional[timedelta] = None
    catchup: bool = False
    run_once_start_time: Optional[datetime] = None

    orchestrator_id: Optional[UUID]
    pipeline_id: Optional[UUID]

    @field_validator(
        "start_time", "end_time", "run_once_start_time", mode="after"
    )
    @classmethod
    def _ensure_tzunaware_utc(
        cls, value: Optional[datetime]
    ) -> Optional[datetime]:
        """Ensures that all datetimes are timezone unaware and in UTC time.

        Args:
            value: The datetime.

        Returns:
            The datetime in UTC time without timezone.
        """
        if value and value.tzinfo:
            value = value.astimezone(timezone.utc)
            value = value.replace(tzinfo=None)

        return value

    @model_validator(mode="after")
    def _ensure_cron_or_periodic_schedule_configured(
        self,
    ) -> "ScheduleRequest":
        """Ensures that the cron expression or start time + interval are set.

        Returns:
            All schedule attributes.

        Raises:
            ValueError: If no cron expression or start time + interval were
                provided.
        """
        cron_expression = self.cron_expression
        periodic_schedule = self.start_time and self.interval_second
        run_once_starts_at = self.run_once_start_time

        if cron_expression and periodic_schedule:
            logger.warning(
                "This schedule was created with a cron expression as well as "
                "values for `start_time` and `interval_seconds`. The resulting "
                "behavior depends on the concrete orchestrator implementation "
                "but will usually ignore the interval and use the cron "
                "expression."
            )
            return self
        elif cron_expression and run_once_starts_at:
            logger.warning(
                "This schedule was created with a cron expression as well as "
                "a value for `run_once_start_time`. The resulting behavior "
                "depends on the concrete orchestrator implementation but will "
                "usually ignore the `run_once_start_time`."
            )
            return self
        elif cron_expression or periodic_schedule or run_once_starts_at:
            return self
        else:
            raise ValueError(
                "Either a cron expression, a start time and interval seconds "
                "or a run once start time "
                "need to be set for a valid schedule."
            )

`ScheduleResponse`

Bases: WorkspaceScopedResponse[ScheduleResponseBody, ScheduleResponseMetadata, ScheduleResponseResources]

Response model for schedules.

Source code in src/zenml/models/v2/core/schedule.py

class ScheduleResponse(
    WorkspaceScopedResponse[
        ScheduleResponseBody,
        ScheduleResponseMetadata,
        ScheduleResponseResources,
    ],
):
    """Response model for schedules."""

    name: str = Field(
        title="Name of this schedule.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    def get_hydrated_version(self) -> "ScheduleResponse":
        """Get the hydrated version of this schedule.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_schedule(self.id)

    # Helper methods
    @property
    def utc_start_time(self) -> Optional[str]:
        """Optional ISO-formatted string of the UTC start time.

        Returns:
            Optional ISO-formatted string of the UTC start time.
        """
        if not self.start_time:
            return None

        return to_utc_timezone(self.start_time).isoformat()

    @property
    def utc_end_time(self) -> Optional[str]:
        """Optional ISO-formatted string of the UTC end time.

        Returns:
            Optional ISO-formatted string of the UTC end time.
        """
        if not self.end_time:
            return None

        return to_utc_timezone(self.end_time).isoformat()

    # Body and metadata properties
    @property
    def active(self) -> bool:
        """The `active` property.

        Returns:
            the value of the property.
        """
        return self.get_body().active

    @property
    def cron_expression(self) -> Optional[str]:
        """The `cron_expression` property.

        Returns:
            the value of the property.
        """
        return self.get_body().cron_expression

    @property
    def start_time(self) -> Optional[datetime]:
        """The `start_time` property.

        Returns:
            the value of the property.
        """
        return self.get_body().start_time

    @property
    def end_time(self) -> Optional[datetime]:
        """The `end_time` property.

        Returns:
            the value of the property.
        """
        return self.get_body().end_time

    @property
    def run_once_start_time(self) -> Optional[datetime]:
        """The `run_once_start_time` property.

        Returns:
            the value of the property.
        """
        return self.get_body().run_once_start_time

    @property
    def interval_second(self) -> Optional[timedelta]:
        """The `interval_second` property.

        Returns:
            the value of the property.
        """
        return self.get_body().interval_second

    @property
    def catchup(self) -> bool:
        """The `catchup` property.

        Returns:
            the value of the property.
        """
        return self.get_body().catchup

    @property
    def orchestrator_id(self) -> Optional[UUID]:
        """The `orchestrator_id` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().orchestrator_id

    @property
    def pipeline_id(self) -> Optional[UUID]:
        """The `pipeline_id` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().pipeline_id

    @property
    def run_metadata(self) -> Dict[str, MetadataType]:
        """The `run_metadata` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().run_metadata

`active` `property`

The active property.

Returns:

Type	Description
`bool`	the value of the property.

`catchup` `property`

The catchup property.

Returns:

Type	Description
`bool`	the value of the property.

`cron_expression` `property`

The cron_expression property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`end_time` `property`

The end_time property.

Returns:

Type	Description
`Optional[datetime]`	the value of the property.

`interval_second` `property`

The interval_second property.

Returns:

Type	Description
`Optional[timedelta]`	the value of the property.

`orchestrator_id` `property`

The orchestrator_id property.

Returns:

Type	Description
`Optional[UUID]`	the value of the property.

`pipeline_id` `property`

The pipeline_id property.

Returns:

Type	Description
`Optional[UUID]`	the value of the property.

`run_metadata` `property`

The run_metadata property.

Returns:

Type	Description
`Dict[str, MetadataType]`	the value of the property.

`run_once_start_time` `property`

The run_once_start_time property.

Returns:

Type	Description
`Optional[datetime]`	the value of the property.

`start_time` `property`

The start_time property.

Returns:

Type	Description
`Optional[datetime]`	the value of the property.

`utc_end_time` `property`

Optional ISO-formatted string of the UTC end time.

Returns:

Type	Description
`Optional[str]`	Optional ISO-formatted string of the UTC end time.

`utc_start_time` `property`

Optional ISO-formatted string of the UTC start time.

Returns:

Type	Description
`Optional[str]`	Optional ISO-formatted string of the UTC start time.

`get_hydrated_version()`

Get the hydrated version of this schedule.

Returns:

Type	Description
`ScheduleResponse`	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/schedule.py

def get_hydrated_version(self) -> "ScheduleResponse":
    """Get the hydrated version of this schedule.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_schedule(self.id)

`ScheduleResponseBody`

Response body for schedules.

Source code in src/zenml/models/v2/core/schedule.py

class ScheduleResponseBody(WorkspaceScopedResponseBody):
    """Response body for schedules."""

    active: bool
    cron_expression: Optional[str] = None
    start_time: Optional[datetime] = None
    end_time: Optional[datetime] = None
    interval_second: Optional[timedelta] = None
    catchup: bool = False
    run_once_start_time: Optional[datetime] = None

`ScheduleResponseMetadata`

Response metadata for schedules.

Source code in src/zenml/models/v2/core/schedule.py

class ScheduleResponseMetadata(WorkspaceScopedResponseMetadata):
    """Response metadata for schedules."""

    orchestrator_id: Optional[UUID]
    pipeline_id: Optional[UUID]

    run_metadata: Dict[str, MetadataType] = Field(
        title="Metadata associated with this schedule.",
        default={},
    )

`ScheduleUpdate`

Bases: BaseUpdate

Update model for schedules.

Source code in src/zenml/models/v2/core/schedule.py

class ScheduleUpdate(BaseUpdate):
    """Update model for schedules."""

    name: Optional[str] = None
    active: Optional[bool] = None
    cron_expression: Optional[str] = None
    start_time: Optional[datetime] = None
    end_time: Optional[datetime] = None
    interval_second: Optional[timedelta] = None
    catchup: Optional[bool] = None
    run_once_start_time: Optional[datetime] = None
    orchestrator_id: Optional[UUID] = None
    pipeline_id: Optional[UUID] = None

`SecretFilter`

Model to enable advanced filtering of all Secrets.

Source code in src/zenml/models/v2/core/secret.py

class SecretFilter(WorkspaceScopedFilter):
    """Model to enable advanced filtering of all Secrets."""

    FILTER_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        *WorkspaceScopedFilter.FILTER_EXCLUDE_FIELDS,
        "values",
    ]

    name: Optional[str] = Field(
        default=None,
        description="Name of the secret",
    )
    scope: Optional[Union[SecretScope, str]] = Field(
        default=None,
        description="Scope in which to filter secrets",
        union_mode="left_to_right",
    )

    @staticmethod
    def _get_filtering_value(value: Optional[Any]) -> str:
        """Convert the value to a string that can be used for lexicographical filtering and sorting.

        Args:
            value: The value to convert.

        Returns:
            The value converted to string format that can be used for
            lexicographical sorting and filtering.
        """
        if value is None:
            return ""
        str_value = str(value)
        if isinstance(value, datetime):
            str_value = value.strftime("%Y-%m-%d %H:%M:%S")
        return str_value

    def secret_matches(self, secret: SecretResponse) -> bool:
        """Checks if a secret matches the filter criteria.

        Args:
            secret: The secret to check.

        Returns:
            True if the secret matches the filter criteria, False otherwise.
        """
        for filter in self.list_of_filters:
            column_value: Optional[Any] = None
            if filter.column == "workspace_id":
                column_value = secret.workspace.id
            elif filter.column == "user_id":
                column_value = secret.user.id if secret.user else None
            else:
                column_value = getattr(secret, filter.column)

            # Convert the values to strings for lexicographical comparison.
            str_column_value = self._get_filtering_value(column_value)
            str_filter_value = self._get_filtering_value(filter.value)

            # Compare the lexicographical values according to the operation.
            if filter.operation == GenericFilterOps.EQUALS:
                result = str_column_value == str_filter_value
            elif filter.operation == GenericFilterOps.CONTAINS:
                result = str_filter_value in str_column_value
            elif filter.operation == GenericFilterOps.STARTSWITH:
                result = str_column_value.startswith(str_filter_value)
            elif filter.operation == GenericFilterOps.ENDSWITH:
                result = str_column_value.endswith(str_filter_value)
            elif filter.operation == GenericFilterOps.GT:
                result = str_column_value > str_filter_value
            elif filter.operation == GenericFilterOps.GTE:
                result = str_column_value >= str_filter_value
            elif filter.operation == GenericFilterOps.LT:
                result = str_column_value < str_filter_value
            elif filter.operation == GenericFilterOps.LTE:
                result = str_column_value <= str_filter_value

            # Exit early if the result is False for AND, and True for OR
            if self.logical_operator == LogicalOperators.AND:
                if not result:
                    return False
            else:
                if result:
                    return True

        # If we get here, all filters have been checked and the result is
        # True for AND, and False for OR
        if self.logical_operator == LogicalOperators.AND:
            return True
        else:
            return False

    def sort_secrets(
        self, secrets: List[SecretResponse]
    ) -> List[SecretResponse]:
        """Sorts a list of secrets according to the filter criteria.

        Args:
            secrets: The list of secrets to sort.

        Returns:
            The sorted list of secrets.
        """
        column, sort_op = self.sorting_params
        sorted_secrets = sorted(
            secrets,
            key=lambda secret: self._get_filtering_value(
                getattr(secret, column)
            ),
            reverse=sort_op == SorterOps.DESCENDING,
        )

        return sorted_secrets

`secret_matches(secret)`

Checks if a secret matches the filter criteria.

Parameters:

Name	Type	Description	Default
`secret`	`SecretResponse`	The secret to check.	required

Returns:

Type	Description
`bool`	True if the secret matches the filter criteria, False otherwise.

Source code in src/zenml/models/v2/core/secret.py

def secret_matches(self, secret: SecretResponse) -> bool:
    """Checks if a secret matches the filter criteria.

    Args:
        secret: The secret to check.

    Returns:
        True if the secret matches the filter criteria, False otherwise.
    """
    for filter in self.list_of_filters:
        column_value: Optional[Any] = None
        if filter.column == "workspace_id":
            column_value = secret.workspace.id
        elif filter.column == "user_id":
            column_value = secret.user.id if secret.user else None
        else:
            column_value = getattr(secret, filter.column)

        # Convert the values to strings for lexicographical comparison.
        str_column_value = self._get_filtering_value(column_value)
        str_filter_value = self._get_filtering_value(filter.value)

        # Compare the lexicographical values according to the operation.
        if filter.operation == GenericFilterOps.EQUALS:
            result = str_column_value == str_filter_value
        elif filter.operation == GenericFilterOps.CONTAINS:
            result = str_filter_value in str_column_value
        elif filter.operation == GenericFilterOps.STARTSWITH:
            result = str_column_value.startswith(str_filter_value)
        elif filter.operation == GenericFilterOps.ENDSWITH:
            result = str_column_value.endswith(str_filter_value)
        elif filter.operation == GenericFilterOps.GT:
            result = str_column_value > str_filter_value
        elif filter.operation == GenericFilterOps.GTE:
            result = str_column_value >= str_filter_value
        elif filter.operation == GenericFilterOps.LT:
            result = str_column_value < str_filter_value
        elif filter.operation == GenericFilterOps.LTE:
            result = str_column_value <= str_filter_value

        # Exit early if the result is False for AND, and True for OR
        if self.logical_operator == LogicalOperators.AND:
            if not result:
                return False
        else:
            if result:
                return True

    # If we get here, all filters have been checked and the result is
    # True for AND, and False for OR
    if self.logical_operator == LogicalOperators.AND:
        return True
    else:
        return False

`sort_secrets(secrets)`

Sorts a list of secrets according to the filter criteria.

Parameters:

Name	Type	Description	Default
`secrets`	`List[SecretResponse]`	The list of secrets to sort.	required

Returns:

Type	Description
`List[SecretResponse]`	The sorted list of secrets.

Source code in src/zenml/models/v2/core/secret.py

def sort_secrets(
    self, secrets: List[SecretResponse]
) -> List[SecretResponse]:
    """Sorts a list of secrets according to the filter criteria.

    Args:
        secrets: The list of secrets to sort.

    Returns:
        The sorted list of secrets.
    """
    column, sort_op = self.sorting_params
    sorted_secrets = sorted(
        secrets,
        key=lambda secret: self._get_filtering_value(
            getattr(secret, column)
        ),
        reverse=sort_op == SorterOps.DESCENDING,
    )

    return sorted_secrets

`SecretRequest`

Request models for secrets.

Source code in src/zenml/models/v2/core/secret.py

class SecretRequest(WorkspaceScopedRequest):
    """Request models for secrets."""

    ANALYTICS_FIELDS: ClassVar[List[str]] = ["scope"]

    name: str = Field(
        title="The name of the secret.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    scope: SecretScope = Field(
        SecretScope.WORKSPACE, title="The scope of the secret."
    )
    values: Dict[str, Optional[PlainSerializedSecretStr]] = Field(
        default_factory=dict, title="The values stored in this secret."
    )

    @property
    def secret_values(self) -> Dict[str, str]:
        """A dictionary with all un-obfuscated values stored in this secret.

        The values are returned as strings, not SecretStr. If a value is
        None, it is not included in the returned dictionary. This is to enable
        the use of None values in the update model to indicate that a secret
        value should be deleted.

        Returns:
            A dictionary containing the secret's values.
        """
        return {
            k: v.get_secret_value()
            for k, v in self.values.items()
            if v is not None
        }

`secret_values` `property`

A dictionary with all un-obfuscated values stored in this secret.

The values are returned as strings, not SecretStr. If a value is None, it is not included in the returned dictionary. This is to enable the use of None values in the update model to indicate that a secret value should be deleted.

Returns:

Type	Description
`Dict[str, str]`	A dictionary containing the secret's values.

`SecretResponse`

Bases: WorkspaceScopedResponse[SecretResponseBody, SecretResponseMetadata, SecretResponseResources]

Response model for secrets.

Source code in src/zenml/models/v2/core/secret.py

class SecretResponse(
    WorkspaceScopedResponse[
        SecretResponseBody, SecretResponseMetadata, SecretResponseResources
    ]
):
    """Response model for secrets."""

    ANALYTICS_FIELDS: ClassVar[List[str]] = ["scope"]

    name: str = Field(
        title="The name of the secret.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    def get_hydrated_version(self) -> "SecretResponse":
        """Get the hydrated version of this workspace.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_secret(self.id)

    # Body and metadata properties

    @property
    def scope(self) -> SecretScope:
        """The `scope` property.

        Returns:
            the value of the property.
        """
        return self.get_body().scope

    @property
    def values(self) -> Dict[str, Optional[SecretStr]]:
        """The `values` property.

        Returns:
            the value of the property.
        """
        return self.get_body().values

    # Helper methods
    @property
    def secret_values(self) -> Dict[str, str]:
        """A dictionary with all un-obfuscated values stored in this secret.

        The values are returned as strings, not SecretStr. If a value is
        None, it is not included in the returned dictionary. This is to enable
        the use of None values in the update model to indicate that a secret
        value should be deleted.

        Returns:
            A dictionary containing the secret's values.
        """
        return {
            k: v.get_secret_value()
            for k, v in self.values.items()
            if v is not None
        }

    @property
    def has_missing_values(self) -> bool:
        """Returns True if the secret has missing values (i.e. None).

        Values can be missing from a secret for example if the user retrieves a
        secret but does not have the permission to view the secret values.

        Returns:
            True if the secret has any values set to None.
        """
        return any(v is None for v in self.values.values())

    def add_secret(self, key: str, value: str) -> None:
        """Adds a secret value to the secret.

        Args:
            key: The key of the secret value.
            value: The secret value.
        """
        self.get_body().values[key] = SecretStr(value)

    def remove_secret(self, key: str) -> None:
        """Removes a secret value from the secret.

        Args:
            key: The key of the secret value.
        """
        del self.get_body().values[key]

    def remove_secrets(self) -> None:
        """Removes all secret values from the secret but keep the keys."""
        self.get_body().values = {k: None for k in self.values.keys()}

    def set_secrets(self, values: Dict[str, str]) -> None:
        """Sets the secret values of the secret.

        Args:
            values: The secret values to set.
        """
        self.get_body().values = {k: SecretStr(v) for k, v in values.items()}

`has_missing_values` `property`

Returns True if the secret has missing values (i.e. None).

Values can be missing from a secret for example if the user retrieves a secret but does not have the permission to view the secret values.

Returns:

Type	Description
`bool`	True if the secret has any values set to None.

`scope` `property`

The scope property.

Returns:

Type	Description
`SecretScope`	the value of the property.

`secret_values` `property`

A dictionary with all un-obfuscated values stored in this secret.

The values are returned as strings, not SecretStr. If a value is None, it is not included in the returned dictionary. This is to enable the use of None values in the update model to indicate that a secret value should be deleted.

Returns:

Type	Description
`Dict[str, str]`	A dictionary containing the secret's values.

`values` `property`

The values property.

Returns:

Type	Description
`Dict[str, Optional[SecretStr]]`	the value of the property.

`add_secret(key, value)`

Adds a secret value to the secret.

Parameters:

Name	Type	Description	Default
`key`	`str`	The key of the secret value.	required
`value`	`str`	The secret value.	required

Source code in src/zenml/models/v2/core/secret.py

def add_secret(self, key: str, value: str) -> None:
    """Adds a secret value to the secret.

    Args:
        key: The key of the secret value.
        value: The secret value.
    """
    self.get_body().values[key] = SecretStr(value)

`get_hydrated_version()`

Get the hydrated version of this workspace.

Returns:

Type	Description
`SecretResponse`	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/secret.py

def get_hydrated_version(self) -> "SecretResponse":
    """Get the hydrated version of this workspace.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_secret(self.id)

`remove_secret(key)`

Removes a secret value from the secret.

Parameters:

Name	Type	Description	Default
`key`	`str`	The key of the secret value.	required

Source code in src/zenml/models/v2/core/secret.py

def remove_secret(self, key: str) -> None:
    """Removes a secret value from the secret.

    Args:
        key: The key of the secret value.
    """
    del self.get_body().values[key]

`remove_secrets()`

Removes all secret values from the secret but keep the keys.

Source code in src/zenml/models/v2/core/secret.py

def remove_secrets(self) -> None:
    """Removes all secret values from the secret but keep the keys."""
    self.get_body().values = {k: None for k in self.values.keys()}

`set_secrets(values)`

Sets the secret values of the secret.

Parameters:

Name	Type	Description	Default
`values`	`Dict[str, str]`	The secret values to set.	required

Source code in src/zenml/models/v2/core/secret.py

def set_secrets(self, values: Dict[str, str]) -> None:
    """Sets the secret values of the secret.

    Args:
        values: The secret values to set.
    """
    self.get_body().values = {k: SecretStr(v) for k, v in values.items()}

`SecretResponseBody`

Response body for secrets.

Source code in src/zenml/models/v2/core/secret.py

class SecretResponseBody(WorkspaceScopedResponseBody):
    """Response body for secrets."""

    scope: SecretScope = Field(
        SecretScope.WORKSPACE, title="The scope of the secret."
    )
    values: Dict[str, Optional[PlainSerializedSecretStr]] = Field(
        default_factory=dict, title="The values stored in this secret."
    )

`SecretResponseMetadata`

Response metadata for secrets.

Source code in src/zenml/models/v2/core/secret.py

class SecretResponseMetadata(WorkspaceScopedResponseMetadata):
    """Response metadata for secrets."""

`SecretUpdate`

Bases: BaseUpdate

Secret update model.

Source code in src/zenml/models/v2/core/secret.py

class SecretUpdate(BaseUpdate):
    """Secret update model."""

    ANALYTICS_FIELDS: ClassVar[List[str]] = ["scope"]

    name: Optional[str] = Field(
        title="The name of the secret.",
        max_length=STR_FIELD_MAX_LENGTH,
        default=None,
    )
    scope: Optional[SecretScope] = Field(
        default=None, title="The scope of the secret."
    )
    values: Optional[Dict[str, Optional[PlainSerializedSecretStr]]] = Field(
        title="The values stored in this secret.",
        default=None,
    )

    def get_secret_values_update(self) -> Dict[str, Optional[str]]:
        """Returns a dictionary with the secret values to update.

        Returns:
            A dictionary with the secret values to update.
        """
        if self.values is not None:
            return {
                k: v.get_secret_value() if v is not None else None
                for k, v in self.values.items()
            }

        return {}

`get_secret_values_update()`

Returns a dictionary with the secret values to update.

Returns:

Type	Description
`Dict[str, Optional[str]]`	A dictionary with the secret values to update.

Source code in src/zenml/models/v2/core/secret.py

def get_secret_values_update(self) -> Dict[str, Optional[str]]:
    """Returns a dictionary with the secret values to update.

    Returns:
        A dictionary with the secret values to update.
    """
    if self.values is not None:
        return {
            k: v.get_secret_value() if v is not None else None
            for k, v in self.values.items()
        }

    return {}

`ServerActivationRequest`

Bases: ServerSettingsUpdate

Model for activating the server.

Source code in src/zenml/models/v2/core/server_settings.py

class ServerActivationRequest(ServerSettingsUpdate):
    """Model for activating the server."""

    admin_username: Optional[str] = Field(
        default=None,
        title="The username of the default admin account to create. Leave "
        "empty to skip creating the default admin account.",
    )

    admin_password: Optional[str] = Field(
        default=None,
        title="The password of the default admin account to create. Leave "
        "empty to skip creating the default admin account.",
    )

`ServerDatabaseType`

Bases: StrEnum

Enum for server database types.

Source code in src/zenml/models/v2/misc/server_models.py

class ServerDatabaseType(StrEnum):
    """Enum for server database types."""

    SQLITE = "sqlite"
    MYSQL = "mysql"
    OTHER = "other"

`ServerDeploymentType`

Bases: StrEnum

Enum for server deployment types.

Source code in src/zenml/models/v2/misc/server_models.py

class ServerDeploymentType(StrEnum):
    """Enum for server deployment types."""

    LOCAL = "local"
    DOCKER = "docker"
    KUBERNETES = "kubernetes"
    AWS = "aws"
    GCP = "gcp"
    AZURE = "azure"
    ALPHA = "alpha"
    OTHER = "other"
    HF_SPACES = "hf_spaces"
    SANDBOX = "sandbox"
    CLOUD = "cloud"

`ServerLoadInfo`

Bases: BaseModel

Domain model for ZenML server load information.

Source code in src/zenml/models/v2/misc/server_models.py

class ServerLoadInfo(BaseModel):
    """Domain model for ZenML server load information."""

    threads: int = Field(
        title="Number of threads that the server is currently using."
    )

    db_connections_total: int = Field(
        title="Total number of database connections (active and idle) that the "
        "server currently has established."
    )

    db_connections_active: int = Field(
        title="Number of database connections that the server is currently "
        "actively using to make queries or transactions."
    )

    db_connections_overflow: int = Field(
        title="Number of overflow database connections that the server is "
        "currently actively using to make queries or transactions."
    )

`ServerModel`

Bases: BaseModel

Domain model for ZenML servers.

Source code in src/zenml/models/v2/misc/server_models.py

class ServerModel(BaseModel):
    """Domain model for ZenML servers."""

    id: UUID = Field(default_factory=uuid4, title="The unique server id.")

    name: Optional[str] = Field(None, title="The name of the ZenML server.")

    version: str = Field(
        title="The ZenML version that the server is running.",
    )

    active: bool = Field(
        True, title="Flag to indicate whether the server is active."
    )

    debug: bool = Field(
        False, title="Flag to indicate whether ZenML is running on debug mode."
    )

    deployment_type: ServerDeploymentType = Field(
        ServerDeploymentType.OTHER,
        title="The ZenML server deployment type.",
    )
    database_type: ServerDatabaseType = Field(
        ServerDatabaseType.OTHER,
        title="The database type that the server is using.",
    )
    secrets_store_type: SecretsStoreType = Field(
        SecretsStoreType.NONE,
        title="The type of secrets store that the server is using.",
    )
    auth_scheme: AuthScheme = Field(
        title="The authentication scheme that the server is using.",
    )
    server_url: str = Field(
        "",
        title="The URL where the ZenML server API is reachable. If not "
        "specified, the clients will use the same URL used to connect them to "
        "the ZenML server.",
    )
    dashboard_url: str = Field(
        "",
        title="The URL where the ZenML dashboard is reachable. If "
        "not specified, the `server_url` value will be used instead.",
    )
    analytics_enabled: bool = Field(
        default=True,  # We set a default for migrations from < 0.57.0
        title="Enable server-side analytics.",
    )

    metadata: Dict[str, str] = Field(
        {},
        title="The metadata associated with the server.",
    )

    last_user_activity: Optional[datetime] = Field(
        None,
        title="Timestamp of latest user activity traced on the server.",
    )

    pro_dashboard_url: Optional[str] = Field(
        None,
        title="The base URL of the ZenML Pro dashboard to which the server "
        "is connected. Only set if the server is a ZenML Pro server.",
    )

    pro_api_url: Optional[str] = Field(
        None,
        title="The base URL of the ZenML Pro API to which the server is "
        "connected. Only set if the server is a ZenML Pro server.",
    )

    pro_organization_id: Optional[UUID] = Field(
        None,
        title="The ID of the ZenML Pro organization to which the server is "
        "connected. Only set if the server is a ZenML Pro server.",
    )

    pro_organization_name: Optional[str] = Field(
        None,
        title="The name of the ZenML Pro organization to which the server is "
        "connected. Only set if the server is a ZenML Pro server.",
    )

    pro_tenant_id: Optional[UUID] = Field(
        None,
        title="The ID of the ZenML Pro tenant to which the server is connected. "
        "Only set if the server is a ZenML Pro server.",
    )

    pro_tenant_name: Optional[str] = Field(
        None,
        title="The name of the ZenML Pro tenant to which the server is connected. "
        "Only set if the server is a ZenML Pro server.",
    )

    def is_local(self) -> bool:
        """Return whether the server is running locally.

        Returns:
            True if the server is running locally, False otherwise.
        """
        from zenml.config.global_config import GlobalConfiguration

        # Local ZenML servers are identifiable by the fact that their
        # server ID is the same as the local client (user) ID.
        return self.id == GlobalConfiguration().user_id

    def is_pro_server(self) -> bool:
        """Return whether the server is a ZenML Pro server.

        Returns:
            True if the server is a ZenML Pro server, False otherwise.
        """
        return self.deployment_type == ServerDeploymentType.CLOUD

`is_local()`

Return whether the server is running locally.

Returns:

Type	Description
`bool`	True if the server is running locally, False otherwise.

Source code in src/zenml/models/v2/misc/server_models.py

def is_local(self) -> bool:
    """Return whether the server is running locally.

    Returns:
        True if the server is running locally, False otherwise.
    """
    from zenml.config.global_config import GlobalConfiguration

    # Local ZenML servers are identifiable by the fact that their
    # server ID is the same as the local client (user) ID.
    return self.id == GlobalConfiguration().user_id

`is_pro_server()`

Return whether the server is a ZenML Pro server.

Returns:

Type	Description
`bool`	True if the server is a ZenML Pro server, False otherwise.

Source code in src/zenml/models/v2/misc/server_models.py

def is_pro_server(self) -> bool:
    """Return whether the server is a ZenML Pro server.

    Returns:
        True if the server is a ZenML Pro server, False otherwise.
    """
    return self.deployment_type == ServerDeploymentType.CLOUD

`ServerSettingsResponse`

Bases: BaseResponse[ServerSettingsResponseBody, ServerSettingsResponseMetadata, ServerSettingsResponseResources]

Response model for server settings.

Source code in src/zenml/models/v2/core/server_settings.py

class ServerSettingsResponse(
    BaseResponse[
        ServerSettingsResponseBody,
        ServerSettingsResponseMetadata,
        ServerSettingsResponseResources,
    ]
):
    """Response model for server settings."""

    def get_hydrated_version(self) -> "ServerSettingsResponse":
        """Get the hydrated version of the server settings.

        Returns:
            An instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_server_settings(hydrate=True)

    # Body and metadata properties

    @property
    def server_id(self) -> UUID:
        """The `server_id` property.

        Returns:
            the value of the property.
        """
        return self.get_body().server_id

    @property
    def server_name(self) -> str:
        """The `server_name` property.

        Returns:
            the value of the property.
        """
        return self.get_body().server_name

    @property
    def logo_url(self) -> Optional[str]:
        """The `logo_url` property.

        Returns:
            the value of the property.
        """
        return self.get_body().logo_url

    @property
    def enable_analytics(self) -> bool:
        """The `enable_analytics` property.

        Returns:
            the value of the property.
        """
        return self.get_body().enable_analytics

    @property
    def display_announcements(self) -> Optional[bool]:
        """The `display_announcements` property.

        Returns:
            the value of the property.
        """
        return self.get_body().display_announcements

    @property
    def display_updates(self) -> Optional[bool]:
        """The `display_updates` property.

        Returns:
            the value of the property.
        """
        return self.get_body().display_updates

    @property
    def active(self) -> bool:
        """The `active` property.

        Returns:
            the value of the property.
        """
        return self.get_body().active

    @property
    def last_user_activity(self) -> datetime:
        """The `last_user_activity` property.

        Returns:
            the value of the property.
        """
        return self.get_body().last_user_activity

    @property
    def updated(self) -> datetime:
        """The `updated` property.

        Returns:
            the value of the property.
        """
        return self.get_body().updated

`active` `property`

The active property.

Returns:

Type	Description
`bool`	the value of the property.

`display_announcements` `property`

The display_announcements property.

Returns:

Type	Description
`Optional[bool]`	the value of the property.

`display_updates` `property`

The display_updates property.

Returns:

Type	Description
`Optional[bool]`	the value of the property.

`enable_analytics` `property`

The enable_analytics property.

Returns:

Type	Description
`bool`	the value of the property.

`last_user_activity` `property`

The last_user_activity property.

Returns:

Type	Description
`datetime`	the value of the property.

`logo_url` `property`

The logo_url property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`server_id` `property`

The server_id property.

Returns:

Type	Description
`UUID`	the value of the property.

`server_name` `property`

The server_name property.

Returns:

Type	Description
`str`	the value of the property.

`updated` `property`

The updated property.

Returns:

Type	Description
`datetime`	the value of the property.

`get_hydrated_version()`

Get the hydrated version of the server settings.

Returns:

Type	Description
`ServerSettingsResponse`	An instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/server_settings.py

def get_hydrated_version(self) -> "ServerSettingsResponse":
    """Get the hydrated version of the server settings.

    Returns:
        An instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_server_settings(hydrate=True)

`ServerSettingsResponseBody`

Bases: BaseResponseBody

Response body for server settings.

Source code in src/zenml/models/v2/core/server_settings.py

class ServerSettingsResponseBody(BaseResponseBody):
    """Response body for server settings."""

    server_id: UUID = Field(
        title="The unique server id.",
    )
    server_name: str = Field(title="The name of the server.")
    logo_url: Optional[str] = Field(
        default=None, title="The logo URL of the server."
    )
    active: bool = Field(
        title="Whether the server has been activated or not.",
    )
    enable_analytics: bool = Field(
        title="Whether analytics are enabled for the server.",
    )
    display_announcements: Optional[bool] = Field(
        title="Whether to display announcements about ZenML in the dashboard.",
    )
    display_updates: Optional[bool] = Field(
        title="Whether to display notifications about ZenML updates in the dashboard.",
    )
    last_user_activity: datetime = Field(
        title="The timestamp when the last user activity was detected.",
    )
    updated: datetime = Field(
        title="The timestamp when this resource was last updated."
    )

`ServerSettingsResponseMetadata`

Bases: BaseResponseMetadata

Response metadata for server settings.

Source code in src/zenml/models/v2/core/server_settings.py

class ServerSettingsResponseMetadata(BaseResponseMetadata):
    """Response metadata for server settings."""

`ServerSettingsResponseResources`

Bases: BaseResponseResources

Response resources for server settings.

Source code in src/zenml/models/v2/core/server_settings.py

class ServerSettingsResponseResources(BaseResponseResources):
    """Response resources for server settings."""

`ServerSettingsUpdate`

Bases: BaseZenModel

Model for updating server settings.

Source code in src/zenml/models/v2/core/server_settings.py

class ServerSettingsUpdate(BaseZenModel):
    """Model for updating server settings."""

    server_name: Optional[str] = Field(
        default=None, title="The name of the server."
    )
    logo_url: Optional[str] = Field(
        default=None, title="The logo URL of the server."
    )
    enable_analytics: Optional[bool] = Field(
        default=None,
        title="Whether to enable analytics for the server.",
    )
    display_announcements: Optional[bool] = Field(
        default=None,
        title="Whether to display announcements about ZenML in the dashboard.",
    )
    display_updates: Optional[bool] = Field(
        default=None,
        title="Whether to display notifications about ZenML updates in the dashboard.",
    )

`ServiceAccountFilter`

Bases: BaseFilter

Model to enable advanced filtering of service accounts.

Source code in src/zenml/models/v2/core/service_account.py

class ServiceAccountFilter(BaseFilter):
    """Model to enable advanced filtering of service accounts."""

    name: Optional[str] = Field(
        default=None,
        description="Name of the user",
    )
    description: Optional[str] = Field(
        default=None,
        title="Filter by the service account description.",
    )
    active: Optional[Union[bool, str]] = Field(
        default=None,
        description="Whether the user is active",
        union_mode="left_to_right",
    )

    def apply_filter(
        self,
        query: AnyQuery,
        table: Type["AnySchema"],
    ) -> AnyQuery:
        """Override to filter out user accounts from the query.

        Args:
            query: The query to which to apply the filter.
            table: The query table.

        Returns:
            The query with filter applied.
        """
        query = super().apply_filter(query=query, table=table)
        query = query.where(
            getattr(table, "is_service_account") == True  # noqa: E712
        )

        return query

`apply_filter(query, table)`

Override to filter out user accounts from the query.

Parameters:

Name	Type	Description	Default
`query`	`AnyQuery`	The query to which to apply the filter.	required
`table`	`Type[AnySchema]`	The query table.	required

Returns:

Type	Description
`AnyQuery`	The query with filter applied.

Source code in src/zenml/models/v2/core/service_account.py

def apply_filter(
    self,
    query: AnyQuery,
    table: Type["AnySchema"],
) -> AnyQuery:
    """Override to filter out user accounts from the query.

    Args:
        query: The query to which to apply the filter.
        table: The query table.

    Returns:
        The query with filter applied.
    """
    query = super().apply_filter(query=query, table=table)
    query = query.where(
        getattr(table, "is_service_account") == True  # noqa: E712
    )

    return query

`ServiceAccountRequest`

Bases: BaseRequest

Request model for service accounts.

Source code in src/zenml/models/v2/core/service_account.py

class ServiceAccountRequest(BaseRequest):
    """Request model for service accounts."""

    ANALYTICS_FIELDS: ClassVar[List[str]] = [
        "name",
        "active",
    ]

    name: str = Field(
        title="The unique name for the service account.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    description: Optional[str] = Field(
        default=None,
        title="A description of the service account.",
        max_length=TEXT_FIELD_MAX_LENGTH,
    )
    active: bool = Field(title="Whether the service account is active or not.")
    model_config = ConfigDict(validate_assignment=True, extra="ignore")

`ServiceAccountResponse`

Bases: BaseIdentifiedResponse[ServiceAccountResponseBody, ServiceAccountResponseMetadata, ServiceAccountResponseResources]

Response model for service accounts.

Source code in src/zenml/models/v2/core/service_account.py

class ServiceAccountResponse(
    BaseIdentifiedResponse[
        ServiceAccountResponseBody,
        ServiceAccountResponseMetadata,
        ServiceAccountResponseResources,
    ]
):
    """Response model for service accounts."""

    ANALYTICS_FIELDS: ClassVar[List[str]] = [
        "name",
        "active",
    ]

    name: str = Field(
        title="The unique username for the account.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    def get_hydrated_version(self) -> "ServiceAccountResponse":
        """Get the hydrated version of this service account.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_service_account(self.id)

    def to_user_model(self) -> "UserResponse":
        """Converts the service account to a user model.

        For now, a lot of code still relies on the active user and resource
        owners being a UserResponse object, which is a superset of the
        ServiceAccountResponse object. We need this method to convert the
        service account to a user.

        Returns:
            The user model.
        """
        from zenml.models.v2.core.user import (
            UserResponse,
            UserResponseBody,
            UserResponseMetadata,
        )

        return UserResponse(
            id=self.id,
            name=self.name,
            body=UserResponseBody(
                active=self.active,
                is_service_account=True,
                email_opted_in=False,
                created=self.created,
                updated=self.updated,
                is_admin=False,
            ),
            metadata=UserResponseMetadata(
                description=self.description,
            ),
        )

    # Body and metadata properties
    @property
    def active(self) -> bool:
        """The `active` property.

        Returns:
            the value of the property.
        """
        return self.get_body().active

    @property
    def description(self) -> str:
        """The `description` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().description

`active` `property`

The active property.

Returns:

Type	Description
`bool`	the value of the property.

`description` `property`

The description property.

Returns:

Type	Description
`str`	the value of the property.

`get_hydrated_version()`

Get the hydrated version of this service account.

Returns:

Type	Description
`ServiceAccountResponse`	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/service_account.py

def get_hydrated_version(self) -> "ServiceAccountResponse":
    """Get the hydrated version of this service account.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_service_account(self.id)

`to_user_model()`

Converts the service account to a user model.

For now, a lot of code still relies on the active user and resource owners being a UserResponse object, which is a superset of the ServiceAccountResponse object. We need this method to convert the service account to a user.

Returns:

Type	Description
`UserResponse`	The user model.

Source code in src/zenml/models/v2/core/service_account.py

def to_user_model(self) -> "UserResponse":
    """Converts the service account to a user model.

    For now, a lot of code still relies on the active user and resource
    owners being a UserResponse object, which is a superset of the
    ServiceAccountResponse object. We need this method to convert the
    service account to a user.

    Returns:
        The user model.
    """
    from zenml.models.v2.core.user import (
        UserResponse,
        UserResponseBody,
        UserResponseMetadata,
    )

    return UserResponse(
        id=self.id,
        name=self.name,
        body=UserResponseBody(
            active=self.active,
            is_service_account=True,
            email_opted_in=False,
            created=self.created,
            updated=self.updated,
            is_admin=False,
        ),
        metadata=UserResponseMetadata(
            description=self.description,
        ),
    )

`ServiceAccountResponseBody`

Response body for service accounts.

Source code in src/zenml/models/v2/core/service_account.py

class ServiceAccountResponseBody(BaseDatedResponseBody):
    """Response body for service accounts."""

    active: bool = Field(default=False, title="Whether the account is active.")

`ServiceAccountResponseMetadata`

Bases: BaseResponseMetadata

Response metadata for service accounts.

Source code in src/zenml/models/v2/core/service_account.py

class ServiceAccountResponseMetadata(BaseResponseMetadata):
    """Response metadata for service accounts."""

    description: str = Field(
        default="",
        title="A description of the service account.",
        max_length=TEXT_FIELD_MAX_LENGTH,
    )

`ServiceAccountUpdate`

Bases: BaseUpdate

Update model for service accounts.

Source code in src/zenml/models/v2/core/service_account.py

class ServiceAccountUpdate(BaseUpdate):
    """Update model for service accounts."""

    ANALYTICS_FIELDS: ClassVar[List[str]] = ["name", "active"]

    name: Optional[str] = Field(
        title="The unique name for the service account.",
        max_length=STR_FIELD_MAX_LENGTH,
        default=None,
    )
    description: Optional[str] = Field(
        title="A description of the service account.",
        max_length=TEXT_FIELD_MAX_LENGTH,
        default=None,
    )
    active: Optional[bool] = Field(
        title="Whether the service account is active or not.",
        default=None,
    )

    model_config = ConfigDict(validate_assignment=True)

`ServiceConnectorFilter`

Model to enable advanced filtering of service connectors.

Source code in src/zenml/models/v2/core/service_connector.py

class ServiceConnectorFilter(WorkspaceScopedFilter):
    """Model to enable advanced filtering of service connectors."""

    FILTER_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        *WorkspaceScopedFilter.FILTER_EXCLUDE_FIELDS,
        "scope_type",
        "resource_type",
        "labels_str",
        "labels",
    ]
    CLI_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        *WorkspaceScopedFilter.CLI_EXCLUDE_FIELDS,
        "scope_type",
        "labels_str",
        "labels",
    ]
    scope_type: Optional[str] = Field(
        default=None,
        description="The type to scope this query to.",
    )
    name: Optional[str] = Field(
        default=None,
        description="The name to filter by",
    )
    connector_type: Optional[str] = Field(
        default=None,
        description="The type of service connector to filter by",
    )
    auth_method: Optional[str] = Field(
        default=None,
        title="Filter by the authentication method configured for the "
        "connector",
    )
    resource_type: Optional[str] = Field(
        default=None,
        title="Filter by the type of resource that the connector can be used "
        "to access",
    )
    resource_id: Optional[str] = Field(
        default=None,
        title="Filter by the ID of the resource instance that the connector "
        "is configured to access",
    )
    labels_str: Optional[str] = Field(
        default=None,
        title="Filter by one or more labels. This field can be either a JSON "
        "formatted dictionary of label names and values, where the values are "
        'optional and can be set to None (e.g. `{"label1":"value1", "label2": '
        "null}` ), or a comma-separated list of label names and values (e.g "
        "`label1=value1,label2=`. If a label name is specified without a "
        "value, the filter will match all service connectors that have that "
        "label present, regardless of value.",
    )
    secret_id: Optional[Union[UUID, str]] = Field(
        default=None,
        title="Filter by the ID of the secret that contains the service "
        "connector's credentials",
        union_mode="left_to_right",
    )

    # Use this internally to configure and access the labels as a dictionary
    labels: Optional[Dict[str, Optional[str]]] = Field(
        default=None,
        title="The labels to filter by, as a dictionary",
        exclude=True,
    )

    @model_validator(mode="after")
    def validate_labels(self) -> "ServiceConnectorFilter":
        """Parse the labels string into a label dictionary and vice-versa.

        Returns:
            The validated values.
        """
        if self.labels_str is not None:
            try:
                self.labels = json.loads(self.labels_str)
            except json.JSONDecodeError:
                # Interpret as comma-separated values instead
                self.labels = {
                    label.split("=", 1)[0]: label.split("=", 1)[1]
                    if "=" in label
                    else None
                    for label in self.labels_str.split(",")
                }
        elif self.labels is not None:
            self.labels_str = json.dumps(self.labels)

        return self

`validate_labels()`

Parse the labels string into a label dictionary and vice-versa.

Returns:

Type	Description
`ServiceConnectorFilter`	The validated values.

Source code in src/zenml/models/v2/core/service_connector.py

@model_validator(mode="after")
def validate_labels(self) -> "ServiceConnectorFilter":
    """Parse the labels string into a label dictionary and vice-versa.

    Returns:
        The validated values.
    """
    if self.labels_str is not None:
        try:
            self.labels = json.loads(self.labels_str)
        except json.JSONDecodeError:
            # Interpret as comma-separated values instead
            self.labels = {
                label.split("=", 1)[0]: label.split("=", 1)[1]
                if "=" in label
                else None
                for label in self.labels_str.split(",")
            }
    elif self.labels is not None:
        self.labels_str = json.dumps(self.labels)

    return self

`ServiceConnectorInfo`

Bases: BaseModel

Information about the service connector when creating a full stack.

Source code in src/zenml/models/v2/misc/info_models.py

class ServiceConnectorInfo(BaseModel):
    """Information about the service connector when creating a full stack."""

    type: str
    auth_method: str
    configuration: Dict[str, Any] = {}

`ServiceConnectorRequest`

Request model for service connectors.

Source code in src/zenml/models/v2/core/service_connector.py

class ServiceConnectorRequest(WorkspaceScopedRequest):
    """Request model for service connectors."""

    name: str = Field(
        title="The service connector name.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    connector_type: Union[str, "ServiceConnectorTypeModel"] = Field(
        title="The type of service connector.",
        union_mode="left_to_right",
    )
    description: str = Field(
        default="",
        title="The service connector instance description.",
    )
    auth_method: str = Field(
        title="The authentication method that the connector instance uses to "
        "access the resources.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    resource_types: List[str] = Field(
        default_factory=list,
        title="The type(s) of resource that the connector instance can be used "
        "to gain access to.",
    )
    resource_id: Optional[str] = Field(
        default=None,
        title="Uniquely identifies a specific resource instance that the "
        "connector instance can be used to access. If omitted, the connector "
        "instance can be used to access any and all resource instances that "
        "the authentication method and resource type(s) allow.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    supports_instances: bool = Field(
        default=False,
        title="Indicates whether the connector instance can be used to access "
        "multiple instances of the configured resource type.",
    )
    expires_at: Optional[datetime] = Field(
        default=None,
        title="Time when the authentication credentials configured for the "
        "connector expire. If omitted, the credentials do not expire.",
    )
    expires_skew_tolerance: Optional[int] = Field(
        default=None,
        title="The number of seconds of tolerance to apply when checking "
        "whether the authentication credentials configured for the connector "
        "have expired. If omitted, no tolerance is applied.",
    )
    expiration_seconds: Optional[int] = Field(
        default=None,
        title="The duration, in seconds, that the temporary credentials "
        "generated by this connector should remain valid. Only applicable for "
        "connectors and authentication methods that involve generating "
        "temporary credentials from the ones configured in the connector.",
    )
    configuration: Dict[str, Any] = Field(
        default_factory=dict,
        title="The service connector configuration, not including secrets.",
    )
    secrets: Dict[str, Optional[PlainSerializedSecretStr]] = Field(
        default_factory=dict,
        title="The service connector secrets.",
    )
    labels: Dict[str, str] = Field(
        default_factory=dict,
        title="Service connector labels.",
    )

    # Analytics
    ANALYTICS_FIELDS: ClassVar[List[str]] = [
        "connector_type",
        "auth_method",
        "resource_types",
    ]

    def get_analytics_metadata(self) -> Dict[str, Any]:
        """Format the resource types in the analytics metadata.

        Returns:
            Dict of analytics metadata.
        """
        metadata = super().get_analytics_metadata()
        if len(self.resource_types) == 1:
            metadata["resource_types"] = self.resource_types[0]
        else:
            metadata["resource_types"] = ", ".join(self.resource_types)
        metadata["connector_type"] = self.type
        return metadata

    # Helper methods
    @property
    def type(self) -> str:
        """Get the connector type.

        Returns:
            The connector type.
        """
        if isinstance(self.connector_type, str):
            return self.connector_type
        return self.connector_type.connector_type

    @property
    def emojified_connector_type(self) -> str:
        """Get the emojified connector type.

        Returns:
            The emojified connector type.
        """
        if not isinstance(self.connector_type, str):
            return self.connector_type.emojified_connector_type

        return self.connector_type

    @property
    def emojified_resource_types(self) -> List[str]:
        """Get the emojified connector type.

        Returns:
            The emojified connector type.
        """
        if not isinstance(self.connector_type, str):
            return [
                self.connector_type.resource_type_dict[
                    resource_type
                ].emojified_resource_type
                for resource_type in self.resource_types
            ]

        return self.resource_types

    def validate_and_configure_resources(
        self,
        connector_type: "ServiceConnectorTypeModel",
        resource_types: Optional[Union[str, List[str]]] = None,
        resource_id: Optional[str] = None,
        configuration: Optional[Dict[str, Any]] = None,
        secrets: Optional[Dict[str, Optional[SecretStr]]] = None,
    ) -> None:
        """Validate and configure the resources that the connector can be used to access.

        Args:
            connector_type: The connector type specification used to validate
                the connector configuration.
            resource_types: The type(s) of resource that the connector instance
                can be used to access. If omitted, a multi-type connector is
                configured.
            resource_id: Uniquely identifies a specific resource instance that
                the connector instance can be used to access.
            configuration: The connector configuration.
            secrets: The connector secrets.
        """
        _validate_and_configure_resources(
            connector=self,
            connector_type=connector_type,
            resource_types=resource_types,
            resource_id=resource_id,
            configuration=configuration,
            secrets=secrets,
        )

`emojified_connector_type` `property`

Get the emojified connector type.

Returns:

Type	Description
`str`	The emojified connector type.

`emojified_resource_types` `property`

Get the emojified connector type.

Returns:

Type	Description
`List[str]`	The emojified connector type.

`type` `property`

Get the connector type.

Returns:

Type	Description
`str`	The connector type.

`get_analytics_metadata()`

Format the resource types in the analytics metadata.

Returns:

Type	Description
`Dict[str, Any]`	Dict of analytics metadata.

Source code in src/zenml/models/v2/core/service_connector.py

def get_analytics_metadata(self) -> Dict[str, Any]:
    """Format the resource types in the analytics metadata.

    Returns:
        Dict of analytics metadata.
    """
    metadata = super().get_analytics_metadata()
    if len(self.resource_types) == 1:
        metadata["resource_types"] = self.resource_types[0]
    else:
        metadata["resource_types"] = ", ".join(self.resource_types)
    metadata["connector_type"] = self.type
    return metadata

`validate_and_configure_resources(connector_type, resource_types=None, resource_id=None, configuration=None, secrets=None)`

Validate and configure the resources that the connector can be used to access.

Parameters:

Name	Type	Description	Default
`connector_type`	`ServiceConnectorTypeModel`	The connector type specification used to validate the connector configuration.	required
`resource_types`	`Optional[Union[str, List[str]]]`	The type(s) of resource that the connector instance can be used to access. If omitted, a multi-type connector is configured.	`None`
`resource_id`	`Optional[str]`	Uniquely identifies a specific resource instance that the connector instance can be used to access.	`None`
`configuration`	`Optional[Dict[str, Any]]`	The connector configuration.	`None`
`secrets`	`Optional[Dict[str, Optional[SecretStr]]]`	The connector secrets.	`None`

Source code in src/zenml/models/v2/core/service_connector.py

def validate_and_configure_resources(
    self,
    connector_type: "ServiceConnectorTypeModel",
    resource_types: Optional[Union[str, List[str]]] = None,
    resource_id: Optional[str] = None,
    configuration: Optional[Dict[str, Any]] = None,
    secrets: Optional[Dict[str, Optional[SecretStr]]] = None,
) -> None:
    """Validate and configure the resources that the connector can be used to access.

    Args:
        connector_type: The connector type specification used to validate
            the connector configuration.
        resource_types: The type(s) of resource that the connector instance
            can be used to access. If omitted, a multi-type connector is
            configured.
        resource_id: Uniquely identifies a specific resource instance that
            the connector instance can be used to access.
        configuration: The connector configuration.
        secrets: The connector secrets.
    """
    _validate_and_configure_resources(
        connector=self,
        connector_type=connector_type,
        resource_types=resource_types,
        resource_id=resource_id,
        configuration=configuration,
        secrets=secrets,
    )

`ServiceConnectorRequirements`

Bases: BaseModel

Service connector requirements.

Describes requirements that a service connector consumer has for a service connector instance that it needs in order to access a resource.

Attributes:

Name	Type	Description
`connector_type`	`Optional[str]`	The type of service connector that is required. If omitted, any service connector type can be used.
`resource_type`	`str`	The type of resource that the service connector instance must be able to access.
`resource_id_attr`	`Optional[str]`	The name of an attribute in the stack component configuration that contains the resource ID of the resource that the service connector instance must be able to access.

Source code in src/zenml/models/v2/misc/service_connector_type.py

class ServiceConnectorRequirements(BaseModel):
    """Service connector requirements.

    Describes requirements that a service connector consumer has for a
    service connector instance that it needs in order to access a resource.

    Attributes:
        connector_type: The type of service connector that is required. If
            omitted, any service connector type can be used.
        resource_type: The type of resource that the service connector instance
            must be able to access.
        resource_id_attr: The name of an attribute in the stack component
            configuration that contains the resource ID of the resource that
            the service connector instance must be able to access.
    """

    connector_type: Optional[str] = None
    resource_type: str
    resource_id_attr: Optional[str] = None

    def is_satisfied_by(
        self,
        connector: Union[
            "ServiceConnectorResponse", "ServiceConnectorRequest"
        ],
        component: Union["ComponentResponse", "ComponentBase"],
    ) -> Tuple[bool, str]:
        """Check if the requirements are satisfied by a connector.

        Args:
            connector: The connector to check.
            component: The stack component that the connector is associated
                with.

        Returns:
            True if the requirements are satisfied, False otherwise, and a
            message describing the reason for the failure.
        """
        if self.connector_type and self.connector_type != connector.type:
            return (
                False,
                f"connector type '{connector.type}' does not match the "
                f"'{self.connector_type}' connector type specified in the "
                "stack component requirements",
            )
        if self.resource_type not in connector.resource_types:
            return False, (
                f"connector does not provide the '{self.resource_type}' "
                "resource type specified in the stack component requirements. "
                "Only the following resource types are supported: "
                f"{', '.join(connector.resource_types)}"
            )
        if self.resource_id_attr:
            resource_id = component.configuration.get(self.resource_id_attr)
            if not resource_id:
                return (
                    False,
                    f"the '{self.resource_id_attr}' stack component "
                    f"configuration attribute plays the role of resource "
                    f"identifier, but the stack component does not contain a "
                    f"'{self.resource_id_attr}' attribute. Please add the "
                    f"'{self.resource_id_attr}' attribute to the stack "
                    "component configuration and try again.",
                )

        return True, ""

`is_satisfied_by(connector, component)`

Check if the requirements are satisfied by a connector.

Parameters:

Name	Type	Description	Default
`connector`	`Union[ServiceConnectorResponse, ServiceConnectorRequest]`	The connector to check.	required
`component`	`Union[ComponentResponse, ComponentBase]`	The stack component that the connector is associated with.	required

Returns:

Type	Description
`bool`	True if the requirements are satisfied, False otherwise, and a
`str`	message describing the reason for the failure.

Source code in src/zenml/models/v2/misc/service_connector_type.py

def is_satisfied_by(
    self,
    connector: Union[
        "ServiceConnectorResponse", "ServiceConnectorRequest"
    ],
    component: Union["ComponentResponse", "ComponentBase"],
) -> Tuple[bool, str]:
    """Check if the requirements are satisfied by a connector.

    Args:
        connector: The connector to check.
        component: The stack component that the connector is associated
            with.

    Returns:
        True if the requirements are satisfied, False otherwise, and a
        message describing the reason for the failure.
    """
    if self.connector_type and self.connector_type != connector.type:
        return (
            False,
            f"connector type '{connector.type}' does not match the "
            f"'{self.connector_type}' connector type specified in the "
            "stack component requirements",
        )
    if self.resource_type not in connector.resource_types:
        return False, (
            f"connector does not provide the '{self.resource_type}' "
            "resource type specified in the stack component requirements. "
            "Only the following resource types are supported: "
            f"{', '.join(connector.resource_types)}"
        )
    if self.resource_id_attr:
        resource_id = component.configuration.get(self.resource_id_attr)
        if not resource_id:
            return (
                False,
                f"the '{self.resource_id_attr}' stack component "
                f"configuration attribute plays the role of resource "
                f"identifier, but the stack component does not contain a "
                f"'{self.resource_id_attr}' attribute. Please add the "
                f"'{self.resource_id_attr}' attribute to the stack "
                "component configuration and try again.",
            )

    return True, ""

`ServiceConnectorResourcesInfo`

Bases: BaseModel

Information about the service connector resources needed for CLI and UI.

Source code in src/zenml/models/v2/misc/info_models.py

class ServiceConnectorResourcesInfo(BaseModel):
    """Information about the service connector resources needed for CLI and UI."""

    connector_type: str

    components_resources_info: Dict[StackComponentType, List[ResourcesInfo]]

`ServiceConnectorResourcesModel`

Bases: BaseModel

Service connector resources list.

Lists the resource types and resource instances that a service connector can provide access to.

Source code in src/zenml/models/v2/misc/service_connector_type.py

class ServiceConnectorResourcesModel(BaseModel):
    """Service connector resources list.

    Lists the resource types and resource instances that a service connector
    can provide access to.
    """

    id: Optional[UUID] = Field(
        default=None,
        title="The ID of the service connector instance providing this "
        "resource.",
    )

    name: Optional[str] = Field(
        default=None,
        title="The name of the service connector instance providing this "
        "resource.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    connector_type: Union[str, "ServiceConnectorTypeModel"] = Field(
        title="The type of service connector.", union_mode="left_to_right"
    )

    resources: List[ServiceConnectorTypedResourcesModel] = Field(
        default_factory=list,
        title="The list of resources that the service connector instance can "
        "give access to. Contains one entry for every resource type "
        "that the connector is configured for.",
    )

    error: Optional[str] = Field(
        default=None,
        title="A global error message describing why the service connector "
        "instance could not authenticate to the remote service.",
    )

    @property
    def resources_dict(self) -> Dict[str, ServiceConnectorTypedResourcesModel]:
        """Get the resources as a dictionary indexed by resource type.

        Returns:
            The resources as a dictionary indexed by resource type.
        """
        return {
            resource.resource_type: resource for resource in self.resources
        }

    @property
    def resource_types(self) -> List[str]:
        """Get the resource types.

        Returns:
            The resource types.
        """
        return [resource.resource_type for resource in self.resources]

    def set_error(
        self, error: str, resource_type: Optional[str] = None
    ) -> None:
        """Set a global error message or an error for a single resource type.

        Args:
            error: The error message.
            resource_type: The resource type to set the error message for. If
                omitted, or if there is only one resource type involved, the
                error message is (also) set globally.

        Raises:
            KeyError: If the resource type is not found in the resources list.
        """
        if resource_type:
            resource = self.resources_dict.get(resource_type)
            if not resource:
                raise KeyError(
                    f"resource type '{resource_type}' not found in "
                    "service connector resources list"
                )
            resource.error = error
            resource.resource_ids = None
            if len(self.resources) == 1:
                # If there is only one resource type involved, set the global
                # error message as well.
                self.error = error
        else:
            self.error = error
            for resource in self.resources:
                resource.error = error
                resource.resource_ids = None

    def set_resource_ids(
        self, resource_type: str, resource_ids: List[str]
    ) -> None:
        """Set the resource IDs for a resource type.

        Args:
            resource_type: The resource type to set the resource IDs for.
            resource_ids: The resource IDs to set.

        Raises:
            KeyError: If the resource type is not found in the resources list.
        """
        resource = self.resources_dict.get(resource_type)
        if not resource:
            raise KeyError(
                f"resource type '{resource_type}' not found in "
                "service connector resources list"
            )
        resource.resource_ids = resource_ids
        resource.error = None

    @property
    def type(self) -> str:
        """Get the connector type.

        Returns:
            The connector type.
        """
        if isinstance(self.connector_type, str):
            return self.connector_type
        return self.connector_type.connector_type

    @property
    def emojified_connector_type(self) -> str:
        """Get the emojified connector type.

        Returns:
            The emojified connector type.
        """
        if not isinstance(self.connector_type, str):
            return self.connector_type.emojified_connector_type

        return self.connector_type

    def get_emojified_resource_types(
        self, resource_type: Optional[str] = None
    ) -> List[str]:
        """Get the emojified resource type.

        Args:
            resource_type: The resource type to get the emojified resource type
                for. If omitted, the emojified resource type for all resource
                types is returned.


        Returns:
            The list of emojified resource types.
        """
        if not isinstance(self.connector_type, str):
            if resource_type:
                return [
                    self.connector_type.resource_type_dict[
                        resource_type
                    ].emojified_resource_type
                ]
            return [
                self.connector_type.resource_type_dict[
                    resource_type
                ].emojified_resource_type
                for resource_type in self.resources_dict.keys()
            ]
        if resource_type:
            return [resource_type]
        return list(self.resources_dict.keys())

    def get_default_resource_id(self) -> Optional[str]:
        """Get the default resource ID, if included in the resource list.

        The default resource ID is a resource ID supplied by the connector
        implementation only for resource types that do not support multiple
        instances.

        Returns:
            The default resource ID, or None if no resource ID is set.
        """
        if len(self.resources) != 1:
            # multi-type connectors do not have a default resource ID
            return None

        if isinstance(self.connector_type, str):
            # can't determine default resource ID for unknown connector types
            return None

        resource_type_spec = self.connector_type.resource_type_dict[
            self.resources[0].resource_type
        ]
        if resource_type_spec.supports_instances:
            # resource types that support multiple instances do not have a
            # default resource ID
            return None

        resource_ids = self.resources[0].resource_ids

        if not resource_ids or len(resource_ids) != 1:
            return None

        return resource_ids[0]

    @classmethod
    def from_connector_model(
        cls,
        connector_model: "ServiceConnectorResponse",
        resource_type: Optional[str] = None,
    ) -> "ServiceConnectorResourcesModel":
        """Initialize a resource model from a connector model.

        Args:
            connector_model: The connector model.
            resource_type: The resource type to set on the resource model. If
                omitted, the resource type is set according to the connector
                model.

        Returns:
            A resource list model instance.
        """
        resources = cls(
            id=connector_model.id,
            name=connector_model.name,
            connector_type=connector_model.type,
        )

        resource_types = resource_type or connector_model.resource_types
        for resource_type in resource_types:
            resources.resources.append(
                ServiceConnectorTypedResourcesModel(
                    resource_type=resource_type,
                    resource_ids=[connector_model.resource_id]
                    if connector_model.resource_id
                    else None,
                )
            )

        return resources

`emojified_connector_type` `property`

Get the emojified connector type.

Returns:

Type	Description
`str`	The emojified connector type.

`resource_types` `property`

Get the resource types.

Returns:

Type	Description
`List[str]`	The resource types.

`resources_dict` `property`

Get the resources as a dictionary indexed by resource type.

Returns:

Type	Description
`Dict[str, ServiceConnectorTypedResourcesModel]`	The resources as a dictionary indexed by resource type.

`type` `property`

Get the connector type.

Returns:

Type	Description
`str`	The connector type.

`from_connector_model(connector_model, resource_type=None)` `classmethod`

Initialize a resource model from a connector model.

Parameters:

Name	Type	Description	Default
`connector_model`	`ServiceConnectorResponse`	The connector model.	required
`resource_type`	`Optional[str]`	The resource type to set on the resource model. If omitted, the resource type is set according to the connector model.	`None`

Returns:

Type	Description
`ServiceConnectorResourcesModel`	A resource list model instance.

Source code in src/zenml/models/v2/misc/service_connector_type.py

@classmethod
def from_connector_model(
    cls,
    connector_model: "ServiceConnectorResponse",
    resource_type: Optional[str] = None,
) -> "ServiceConnectorResourcesModel":
    """Initialize a resource model from a connector model.

    Args:
        connector_model: The connector model.
        resource_type: The resource type to set on the resource model. If
            omitted, the resource type is set according to the connector
            model.

    Returns:
        A resource list model instance.
    """
    resources = cls(
        id=connector_model.id,
        name=connector_model.name,
        connector_type=connector_model.type,
    )

    resource_types = resource_type or connector_model.resource_types
    for resource_type in resource_types:
        resources.resources.append(
            ServiceConnectorTypedResourcesModel(
                resource_type=resource_type,
                resource_ids=[connector_model.resource_id]
                if connector_model.resource_id
                else None,
            )
        )

    return resources

`get_default_resource_id()`

Get the default resource ID, if included in the resource list.

The default resource ID is a resource ID supplied by the connector implementation only for resource types that do not support multiple instances.

Returns:

Type	Description
`Optional[str]`	The default resource ID, or None if no resource ID is set.

Source code in src/zenml/models/v2/misc/service_connector_type.py

def get_default_resource_id(self) -> Optional[str]:
    """Get the default resource ID, if included in the resource list.

    The default resource ID is a resource ID supplied by the connector
    implementation only for resource types that do not support multiple
    instances.

    Returns:
        The default resource ID, or None if no resource ID is set.
    """
    if len(self.resources) != 1:
        # multi-type connectors do not have a default resource ID
        return None

    if isinstance(self.connector_type, str):
        # can't determine default resource ID for unknown connector types
        return None

    resource_type_spec = self.connector_type.resource_type_dict[
        self.resources[0].resource_type
    ]
    if resource_type_spec.supports_instances:
        # resource types that support multiple instances do not have a
        # default resource ID
        return None

    resource_ids = self.resources[0].resource_ids

    if not resource_ids or len(resource_ids) != 1:
        return None

    return resource_ids[0]

`get_emojified_resource_types(resource_type=None)`

Get the emojified resource type.

Parameters:

Name	Type	Description	Default
`resource_type`	`Optional[str]`	The resource type to get the emojified resource type for. If omitted, the emojified resource type for all resource types is returned.	`None`

Returns:

Type	Description
`List[str]`	The list of emojified resource types.

Source code in src/zenml/models/v2/misc/service_connector_type.py

def get_emojified_resource_types(
    self, resource_type: Optional[str] = None
) -> List[str]:
    """Get the emojified resource type.

    Args:
        resource_type: The resource type to get the emojified resource type
            for. If omitted, the emojified resource type for all resource
            types is returned.


    Returns:
        The list of emojified resource types.
    """
    if not isinstance(self.connector_type, str):
        if resource_type:
            return [
                self.connector_type.resource_type_dict[
                    resource_type
                ].emojified_resource_type
            ]
        return [
            self.connector_type.resource_type_dict[
                resource_type
            ].emojified_resource_type
            for resource_type in self.resources_dict.keys()
        ]
    if resource_type:
        return [resource_type]
    return list(self.resources_dict.keys())

`set_error(error, resource_type=None)`

Set a global error message or an error for a single resource type.

Parameters:

Name	Type	Description	Default
`error`	`str`	The error message.	required
`resource_type`	`Optional[str]`	The resource type to set the error message for. If omitted, or if there is only one resource type involved, the error message is (also) set globally.	`None`

Raises:

Type	Description
`KeyError`	If the resource type is not found in the resources list.

Source code in src/zenml/models/v2/misc/service_connector_type.py

def set_error(
    self, error: str, resource_type: Optional[str] = None
) -> None:
    """Set a global error message or an error for a single resource type.

    Args:
        error: The error message.
        resource_type: The resource type to set the error message for. If
            omitted, or if there is only one resource type involved, the
            error message is (also) set globally.

    Raises:
        KeyError: If the resource type is not found in the resources list.
    """
    if resource_type:
        resource = self.resources_dict.get(resource_type)
        if not resource:
            raise KeyError(
                f"resource type '{resource_type}' not found in "
                "service connector resources list"
            )
        resource.error = error
        resource.resource_ids = None
        if len(self.resources) == 1:
            # If there is only one resource type involved, set the global
            # error message as well.
            self.error = error
    else:
        self.error = error
        for resource in self.resources:
            resource.error = error
            resource.resource_ids = None

`set_resource_ids(resource_type, resource_ids)`

Set the resource IDs for a resource type.

Parameters:

Name	Type	Description	Default
`resource_type`	`str`	The resource type to set the resource IDs for.	required
`resource_ids`	`List[str]`	The resource IDs to set.	required

Raises:

Type	Description
`KeyError`	If the resource type is not found in the resources list.

Source code in src/zenml/models/v2/misc/service_connector_type.py

def set_resource_ids(
    self, resource_type: str, resource_ids: List[str]
) -> None:
    """Set the resource IDs for a resource type.

    Args:
        resource_type: The resource type to set the resource IDs for.
        resource_ids: The resource IDs to set.

    Raises:
        KeyError: If the resource type is not found in the resources list.
    """
    resource = self.resources_dict.get(resource_type)
    if not resource:
        raise KeyError(
            f"resource type '{resource_type}' not found in "
            "service connector resources list"
        )
    resource.resource_ids = resource_ids
    resource.error = None

`ServiceConnectorResponse`

Bases: WorkspaceScopedResponse[ServiceConnectorResponseBody, ServiceConnectorResponseMetadata, ServiceConnectorResponseResources]

Response model for service connectors.

Source code in src/zenml/models/v2/core/service_connector.py

class ServiceConnectorResponse(
    WorkspaceScopedResponse[
        ServiceConnectorResponseBody,
        ServiceConnectorResponseMetadata,
        ServiceConnectorResponseResources,
    ]
):
    """Response model for service connectors."""

    # Disable the warning for updating responses, because we update the
    # service connector type in place
    _warn_on_response_updates: bool = False

    name: str = Field(
        title="The service connector name.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    def get_analytics_metadata(self) -> Dict[str, Any]:
        """Add the service connector labels to analytics metadata.

        Returns:
            Dict of analytics metadata.
        """
        metadata = super().get_analytics_metadata()

        metadata.update(
            {
                label[6:]: value
                for label, value in self.labels.items()
                if label.startswith("zenml:")
            }
        )
        return metadata

    def get_hydrated_version(self) -> "ServiceConnectorResponse":
        """Get the hydrated version of this service connector.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_service_connector(self.id)

    # Helper methods
    @property
    def type(self) -> str:
        """Get the connector type.

        Returns:
            The connector type.
        """
        if isinstance(self.connector_type, str):
            return self.connector_type
        return self.connector_type.connector_type

    @property
    def emojified_connector_type(self) -> str:
        """Get the emojified connector type.

        Returns:
            The emojified connector type.
        """
        if not isinstance(self.connector_type, str):
            return self.connector_type.emojified_connector_type

        return self.connector_type

    @property
    def emojified_resource_types(self) -> List[str]:
        """Get the emojified connector type.

        Returns:
            The emojified connector type.
        """
        if not isinstance(self.connector_type, str):
            return [
                self.connector_type.resource_type_dict[
                    resource_type
                ].emojified_resource_type
                for resource_type in self.resource_types
            ]

        return self.resource_types

    @property
    def is_multi_type(self) -> bool:
        """Checks if the connector is multi-type.

        A multi-type connector can be used to access multiple types of
        resources.

        Returns:
            True if the connector is multi-type, False otherwise.
        """
        return len(self.resource_types) > 1

    @property
    def is_multi_instance(self) -> bool:
        """Checks if the connector is multi-instance.

        A multi-instance connector is configured to access multiple instances
        of the configured resource type.

        Returns:
            True if the connector is multi-instance, False otherwise.
        """
        return (
            not self.is_multi_type
            and self.supports_instances
            and not self.resource_id
        )

    @property
    def is_single_instance(self) -> bool:
        """Checks if the connector is single-instance.

        A single-instance connector is configured to access only a single
        instance of the configured resource type or does not support multiple
        resource instances.

        Returns:
            True if the connector is single-instance, False otherwise.
        """
        return not self.is_multi_type and not self.is_multi_instance

    @property
    def full_configuration(self) -> Dict[str, str]:
        """Get the full connector configuration, including secrets.

        Returns:
            The full connector configuration, including secrets.
        """
        config = self.configuration.copy()
        config.update(
            {k: v.get_secret_value() for k, v in self.secrets.items() if v}
        )
        return config

    def set_connector_type(
        self, value: Union[str, "ServiceConnectorTypeModel"]
    ) -> None:
        """Auxiliary method to set the connector type.

        Args:
            value: the new value for the connector type.
        """
        self.get_body().connector_type = value

    def validate_and_configure_resources(
        self,
        connector_type: "ServiceConnectorTypeModel",
        resource_types: Optional[Union[str, List[str]]] = None,
        resource_id: Optional[str] = None,
        configuration: Optional[Dict[str, Any]] = None,
        secrets: Optional[Dict[str, Optional[SecretStr]]] = None,
    ) -> None:
        """Validate and configure the resources that the connector can be used to access.

        Args:
            connector_type: The connector type specification used to validate
                the connector configuration.
            resource_types: The type(s) of resource that the connector instance
                can be used to access. If omitted, a multi-type connector is
                configured.
            resource_id: Uniquely identifies a specific resource instance that
                the connector instance can be used to access.
            configuration: The connector configuration.
            secrets: The connector secrets.
        """
        _validate_and_configure_resources(
            connector=self,
            connector_type=connector_type,
            resource_types=resource_types,
            resource_id=resource_id,
            configuration=configuration,
            secrets=secrets,
        )

    # Body and metadata properties
    @property
    def description(self) -> str:
        """The `description` property.

        Returns:
            the value of the property.
        """
        return self.get_body().description

    @property
    def connector_type(self) -> Union[str, "ServiceConnectorTypeModel"]:
        """The `connector_type` property.

        Returns:
            the value of the property.
        """
        return self.get_body().connector_type

    @property
    def auth_method(self) -> str:
        """The `auth_method` property.

        Returns:
            the value of the property.
        """
        return self.get_body().auth_method

    @property
    def resource_types(self) -> List[str]:
        """The `resource_types` property.

        Returns:
            the value of the property.
        """
        return self.get_body().resource_types

    @property
    def resource_id(self) -> Optional[str]:
        """The `resource_id` property.

        Returns:
            the value of the property.
        """
        return self.get_body().resource_id

    @property
    def supports_instances(self) -> bool:
        """The `supports_instances` property.

        Returns:
            the value of the property.
        """
        return self.get_body().supports_instances

    @property
    def expires_at(self) -> Optional[datetime]:
        """The `expires_at` property.

        Returns:
            the value of the property.
        """
        return self.get_body().expires_at

    @property
    def expires_skew_tolerance(self) -> Optional[int]:
        """The `expires_skew_tolerance` property.

        Returns:
            the value of the property.
        """
        return self.get_body().expires_skew_tolerance

    @property
    def configuration(self) -> Dict[str, Any]:
        """The `configuration` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().configuration

    @property
    def secret_id(self) -> Optional[UUID]:
        """The `secret_id` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().secret_id

    @property
    def expiration_seconds(self) -> Optional[int]:
        """The `expiration_seconds` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().expiration_seconds

    @property
    def secrets(self) -> Dict[str, Optional[SecretStr]]:
        """The `secrets` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().secrets

    @property
    def labels(self) -> Dict[str, str]:
        """The `labels` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().labels

`auth_method` `property`

The auth_method property.

Returns:

Type	Description
`str`	the value of the property.

`configuration` `property`

The configuration property.

Returns:

Type	Description
`Dict[str, Any]`	the value of the property.

`connector_type` `property`

The connector_type property.

Returns:

Type	Description
`Union[str, ServiceConnectorTypeModel]`	the value of the property.

`description` `property`

The description property.

Returns:

Type	Description
`str`	the value of the property.

`emojified_connector_type` `property`

Get the emojified connector type.

Returns:

Type	Description
`str`	The emojified connector type.

`emojified_resource_types` `property`

Get the emojified connector type.

Returns:

Type	Description
`List[str]`	The emojified connector type.

`expiration_seconds` `property`

The expiration_seconds property.

Returns:

Type	Description
`Optional[int]`	the value of the property.

`expires_at` `property`

The expires_at property.

Returns:

Type	Description
`Optional[datetime]`	the value of the property.

`expires_skew_tolerance` `property`

The expires_skew_tolerance property.

Returns:

Type	Description
`Optional[int]`	the value of the property.

`full_configuration` `property`

Get the full connector configuration, including secrets.

Returns:

Type	Description
`Dict[str, str]`	The full connector configuration, including secrets.

`is_multi_instance` `property`

Checks if the connector is multi-instance.

A multi-instance connector is configured to access multiple instances of the configured resource type.

Returns:

Type	Description
`bool`	True if the connector is multi-instance, False otherwise.

`is_multi_type` `property`

Checks if the connector is multi-type.

A multi-type connector can be used to access multiple types of resources.

Returns:

Type	Description
`bool`	True if the connector is multi-type, False otherwise.

`is_single_instance` `property`

Checks if the connector is single-instance.

A single-instance connector is configured to access only a single instance of the configured resource type or does not support multiple resource instances.

Returns:

Type	Description
`bool`	True if the connector is single-instance, False otherwise.

`labels` `property`

The labels property.

Returns:

Type	Description
`Dict[str, str]`	the value of the property.

`resource_id` `property`

The resource_id property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`resource_types` `property`

The resource_types property.

Returns:

Type	Description
`List[str]`	the value of the property.

`secret_id` `property`

The secret_id property.

Returns:

Type	Description
`Optional[UUID]`	the value of the property.

`secrets` `property`

The secrets property.

Returns:

Type	Description
`Dict[str, Optional[SecretStr]]`	the value of the property.

`supports_instances` `property`

The supports_instances property.

Returns:

Type	Description
`bool`	the value of the property.

`type` `property`

Get the connector type.

Returns:

Type	Description
`str`	The connector type.

`get_analytics_metadata()`

Add the service connector labels to analytics metadata.

Returns:

Type	Description
`Dict[str, Any]`	Dict of analytics metadata.

Source code in src/zenml/models/v2/core/service_connector.py

def get_analytics_metadata(self) -> Dict[str, Any]:
    """Add the service connector labels to analytics metadata.

    Returns:
        Dict of analytics metadata.
    """
    metadata = super().get_analytics_metadata()

    metadata.update(
        {
            label[6:]: value
            for label, value in self.labels.items()
            if label.startswith("zenml:")
        }
    )
    return metadata

`get_hydrated_version()`

Get the hydrated version of this service connector.

Returns:

Type	Description
`ServiceConnectorResponse`	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/service_connector.py

def get_hydrated_version(self) -> "ServiceConnectorResponse":
    """Get the hydrated version of this service connector.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_service_connector(self.id)

`set_connector_type(value)`

Auxiliary method to set the connector type.

Parameters:

Name	Type	Description	Default
`value`	`Union[str, ServiceConnectorTypeModel]`	the new value for the connector type.	required

Source code in src/zenml/models/v2/core/service_connector.py

def set_connector_type(
    self, value: Union[str, "ServiceConnectorTypeModel"]
) -> None:
    """Auxiliary method to set the connector type.

    Args:
        value: the new value for the connector type.
    """
    self.get_body().connector_type = value

`validate_and_configure_resources(connector_type, resource_types=None, resource_id=None, configuration=None, secrets=None)`

Validate and configure the resources that the connector can be used to access.

Parameters:

Name	Type	Description	Default
`connector_type`	`ServiceConnectorTypeModel`	The connector type specification used to validate the connector configuration.	required
`resource_types`	`Optional[Union[str, List[str]]]`	The type(s) of resource that the connector instance can be used to access. If omitted, a multi-type connector is configured.	`None`
`resource_id`	`Optional[str]`	Uniquely identifies a specific resource instance that the connector instance can be used to access.	`None`
`configuration`	`Optional[Dict[str, Any]]`	The connector configuration.	`None`
`secrets`	`Optional[Dict[str, Optional[SecretStr]]]`	The connector secrets.	`None`

Source code in src/zenml/models/v2/core/service_connector.py

def validate_and_configure_resources(
    self,
    connector_type: "ServiceConnectorTypeModel",
    resource_types: Optional[Union[str, List[str]]] = None,
    resource_id: Optional[str] = None,
    configuration: Optional[Dict[str, Any]] = None,
    secrets: Optional[Dict[str, Optional[SecretStr]]] = None,
) -> None:
    """Validate and configure the resources that the connector can be used to access.

    Args:
        connector_type: The connector type specification used to validate
            the connector configuration.
        resource_types: The type(s) of resource that the connector instance
            can be used to access. If omitted, a multi-type connector is
            configured.
        resource_id: Uniquely identifies a specific resource instance that
            the connector instance can be used to access.
        configuration: The connector configuration.
        secrets: The connector secrets.
    """
    _validate_and_configure_resources(
        connector=self,
        connector_type=connector_type,
        resource_types=resource_types,
        resource_id=resource_id,
        configuration=configuration,
        secrets=secrets,
    )

`ServiceConnectorResponseBody`

Response body for service connectors.

Source code in src/zenml/models/v2/core/service_connector.py

class ServiceConnectorResponseBody(WorkspaceScopedResponseBody):
    """Response body for service connectors."""

    description: str = Field(
        default="",
        title="The service connector instance description.",
    )
    connector_type: Union[str, "ServiceConnectorTypeModel"] = Field(
        title="The type of service connector.", union_mode="left_to_right"
    )
    auth_method: str = Field(
        title="The authentication method that the connector instance uses to "
        "access the resources.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    resource_types: List[str] = Field(
        default_factory=list,
        title="The type(s) of resource that the connector instance can be used "
        "to gain access to.",
    )
    resource_id: Optional[str] = Field(
        default=None,
        title="Uniquely identifies a specific resource instance that the "
        "connector instance can be used to access. If omitted, the connector "
        "instance can be used to access any and all resource instances that "
        "the authentication method and resource type(s) allow.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    supports_instances: bool = Field(
        default=False,
        title="Indicates whether the connector instance can be used to access "
        "multiple instances of the configured resource type.",
    )
    expires_at: Optional[datetime] = Field(
        default=None,
        title="Time when the authentication credentials configured for the "
        "connector expire. If omitted, the credentials do not expire.",
    )
    expires_skew_tolerance: Optional[int] = Field(
        default=None,
        title="The number of seconds of tolerance to apply when checking "
        "whether the authentication credentials configured for the connector "
        "have expired. If omitted, no tolerance is applied.",
    )

`ServiceConnectorResponseMetadata`

Response metadata for service connectors.

Source code in src/zenml/models/v2/core/service_connector.py

class ServiceConnectorResponseMetadata(WorkspaceScopedResponseMetadata):
    """Response metadata for service connectors."""

    configuration: Dict[str, Any] = Field(
        default_factory=dict,
        title="The service connector configuration, not including secrets.",
    )
    secret_id: Optional[UUID] = Field(
        default=None,
        title="The ID of the secret that contains the service connector "
        "secret configuration values.",
    )
    expiration_seconds: Optional[int] = Field(
        default=None,
        title="The duration, in seconds, that the temporary credentials "
        "generated by this connector should remain valid. Only applicable for "
        "connectors and authentication methods that involve generating "
        "temporary credentials from the ones configured in the connector.",
    )
    secrets: Dict[str, Optional[PlainSerializedSecretStr]] = Field(
        default_factory=dict,
        title="The service connector secrets.",
    )
    labels: Dict[str, str] = Field(
        default_factory=dict,
        title="Service connector labels.",
    )

`ServiceConnectorTypeModel`

Bases: BaseModel

Service connector type specification.

Describes the types of resources to which the service connector can be used to gain access and the authentication methods that are supported by the service connector.

The connector type, resource types, resource IDs and authentication methods can all be used as search criteria to lookup and filter service connector instances that are compatible with the requirements of a consumer (e.g. a stack component).

Source code in src/zenml/models/v2/misc/service_connector_type.py

class ServiceConnectorTypeModel(BaseModel):
    """Service connector type specification.

    Describes the types of resources to which the service connector can be used
    to gain access and the authentication methods that are supported by the
    service connector.

    The connector type, resource types, resource IDs and authentication
    methods can all be used as search criteria to lookup and filter service
    connector instances that are compatible with the requirements of a consumer
    (e.g. a stack component).
    """

    name: str = Field(
        title="User readable name for the service connector type.",
    )
    connector_type: str = Field(
        title="The type of service connector. It can be used to represent a "
        "generic resource (e.g. Docker, Kubernetes) or a group of different "
        "resources accessible through a common interface or point of access "
        "and authentication (e.g. a cloud provider or a platform).",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    description: str = Field(
        default="",
        title="A description of the service connector.",
    )
    resource_types: List[ResourceTypeModel] = Field(
        title="A list of resource types that the connector can be used to "
        "access.",
    )
    auth_methods: List[AuthenticationMethodModel] = Field(
        title="A list of specifications describing the authentication "
        "methods that are supported by the service connector, along with the "
        "configuration and secrets attributes that need to be configured for "
        "them.",
    )
    supports_auto_configuration: bool = Field(
        default=False,
        title="Models if the connector can be configured automatically based "
        "on information extracted from a local environment.",
    )
    logo_url: Optional[str] = Field(
        default=None,
        title="Optionally, a URL pointing to a png,"
        "svg or jpg can be attached.",
    )
    emoji: Optional[str] = Field(
        default=None,
        title="Optionally, a python-rich emoji can be attached.",
    )
    docs_url: Optional[str] = Field(
        default=None,
        title="Optionally, a URL pointing to docs, within docs.zenml.io.",
    )
    sdk_docs_url: Optional[str] = Field(
        default=None,
        title="Optionally, a URL pointing to SDK docs,"
        "within sdkdocs.zenml.io.",
    )
    local: bool = Field(
        default=True,
        title="If True, the service connector is available locally.",
    )
    remote: bool = Field(
        default=False,
        title="If True, the service connector is available remotely.",
    )
    _connector_class: Optional[Type["ServiceConnector"]] = None

    @property
    def connector_class(self) -> Optional[Type["ServiceConnector"]]:
        """Get the service connector class.

        Returns:
            The service connector class.
        """
        return self._connector_class

    @property
    def emojified_connector_type(self) -> str:
        """Get the emojified connector type.

        Returns:
            The emojified connector type.
        """
        if not self.emoji:
            return self.connector_type
        return f"{self.emoji} {self.connector_type}"

    @property
    def emojified_resource_types(self) -> List[str]:
        """Get the emojified connector types.

        Returns:
            The emojified connector types.
        """
        return [
            resource_type.emojified_resource_type
            for resource_type in self.resource_types
        ]

    def set_connector_class(
        self, connector_class: Type["ServiceConnector"]
    ) -> None:
        """Set the service connector class.

        Args:
            connector_class: The service connector class.
        """
        self._connector_class = connector_class

    @field_validator("resource_types")
    @classmethod
    def validate_resource_types(
        cls, values: List[ResourceTypeModel]
    ) -> List[ResourceTypeModel]:
        """Validate that the resource types are unique.

        Args:
            values: The list of resource types.

        Returns:
            The list of resource types.

        Raises:
            ValueError: If two or more resource type specifications list the
                same resource type.
        """
        # Gather all resource types from the list of resource type
        # specifications.
        resource_types = [r.resource_type for r in values]
        if len(resource_types) != len(set(resource_types)):
            raise ValueError(
                "Two or more resource type specifications must not list "
                "the same resource type."
            )

        return values

    @field_validator("auth_methods")
    @classmethod
    def validate_auth_methods(
        cls, values: List[AuthenticationMethodModel]
    ) -> List[AuthenticationMethodModel]:
        """Validate that the authentication methods are unique.

        Args:
            values: The list of authentication methods.

        Returns:
            The list of authentication methods.

        Raises:
            ValueError: If two or more authentication method specifications
                share the same authentication method value.
        """
        # Gather all auth methods from the list of auth method
        # specifications.
        auth_methods = [a.auth_method for a in values]
        if len(auth_methods) != len(set(auth_methods)):
            raise ValueError(
                "Two or more authentication method specifications must not "
                "share the same authentication method value."
            )

        return values

    @property
    def resource_type_dict(
        self,
    ) -> Dict[str, ResourceTypeModel]:
        """Returns a map of resource types to resource type specifications.

        Returns:
            A map of resource types to resource type specifications.
        """
        return {r.resource_type: r for r in self.resource_types}

    @property
    def auth_method_dict(
        self,
    ) -> Dict[str, AuthenticationMethodModel]:
        """Returns a map of authentication methods to authentication method specifications.

        Returns:
            A map of authentication methods to authentication method
            specifications.
        """
        return {a.auth_method: a for a in self.auth_methods}

    def find_resource_specifications(
        self,
        auth_method: str,
        resource_type: Optional[str] = None,
    ) -> Tuple[AuthenticationMethodModel, Optional[ResourceTypeModel]]:
        """Find the specifications for a configurable resource.

        Validate the supplied connector configuration parameters against the
        connector specification and return the matching authentication method
        specification and resource specification.

        Args:
            auth_method: The name of the authentication method.
            resource_type: The type of resource being configured.

        Returns:
            The authentication method specification and resource specification
            for the specified authentication method and resource type.

        Raises:
            KeyError: If the authentication method is not supported by the
                connector for the specified resource type and ID.
        """
        # Verify the authentication method
        auth_method_dict = self.auth_method_dict
        if auth_method in auth_method_dict:
            # A match was found for the authentication method
            auth_method_spec = auth_method_dict[auth_method]
        else:
            # No match was found for the authentication method
            raise KeyError(
                f"connector type '{self.connector_type}' does not support the "
                f"'{auth_method}' authentication method. Supported "
                f"authentication methods are: {list(auth_method_dict.keys())}."
            )

        if resource_type is None:
            # No resource type was specified, so no resource type
            # specification can be returned.
            return auth_method_spec, None

        # Verify the resource type
        resource_type_dict = self.resource_type_dict
        if resource_type in resource_type_dict:
            resource_type_spec = resource_type_dict[resource_type]
        else:
            raise KeyError(
                f"connector type '{self.connector_type}' does not support "
                f"resource type '{resource_type}'. Supported resource types "
                f"are: {list(resource_type_dict.keys())}."
            )

        if auth_method not in resource_type_spec.auth_methods:
            raise KeyError(
                f"the '{self.connector_type}' connector type does not support "
                f"the '{auth_method}' authentication method for the "
                f"'{resource_type}' resource type. Supported authentication "
                f"methods are: {resource_type_spec.auth_methods}."
            )

        return auth_method_spec, resource_type_spec

`auth_method_dict` `property`

Returns a map of authentication methods to authentication method specifications.

Returns:

Type	Description
`Dict[str, AuthenticationMethodModel]`	A map of authentication methods to authentication method
`Dict[str, AuthenticationMethodModel]`	specifications.

`connector_class` `property`

Get the service connector class.

Returns:

Type	Description
`Optional[Type[ServiceConnector]]`	The service connector class.

`emojified_connector_type` `property`

Get the emojified connector type.

Returns:

Type	Description
`str`	The emojified connector type.

`emojified_resource_types` `property`

Get the emojified connector types.

Returns:

Type	Description
`List[str]`	The emojified connector types.

`resource_type_dict` `property`

Returns a map of resource types to resource type specifications.

Returns:

Type	Description
`Dict[str, ResourceTypeModel]`	A map of resource types to resource type specifications.

`find_resource_specifications(auth_method, resource_type=None)`

Find the specifications for a configurable resource.

Validate the supplied connector configuration parameters against the connector specification and return the matching authentication method specification and resource specification.

Parameters:

Name	Type	Description	Default
`auth_method`	`str`	The name of the authentication method.	required
`resource_type`	`Optional[str]`	The type of resource being configured.	`None`

Returns:

Type	Description
`AuthenticationMethodModel`	The authentication method specification and resource specification
`Optional[ResourceTypeModel]`	for the specified authentication method and resource type.

Raises:

Type	Description
`KeyError`	If the authentication method is not supported by the connector for the specified resource type and ID.

Source code in src/zenml/models/v2/misc/service_connector_type.py

def find_resource_specifications(
    self,
    auth_method: str,
    resource_type: Optional[str] = None,
) -> Tuple[AuthenticationMethodModel, Optional[ResourceTypeModel]]:
    """Find the specifications for a configurable resource.

    Validate the supplied connector configuration parameters against the
    connector specification and return the matching authentication method
    specification and resource specification.

    Args:
        auth_method: The name of the authentication method.
        resource_type: The type of resource being configured.

    Returns:
        The authentication method specification and resource specification
        for the specified authentication method and resource type.

    Raises:
        KeyError: If the authentication method is not supported by the
            connector for the specified resource type and ID.
    """
    # Verify the authentication method
    auth_method_dict = self.auth_method_dict
    if auth_method in auth_method_dict:
        # A match was found for the authentication method
        auth_method_spec = auth_method_dict[auth_method]
    else:
        # No match was found for the authentication method
        raise KeyError(
            f"connector type '{self.connector_type}' does not support the "
            f"'{auth_method}' authentication method. Supported "
            f"authentication methods are: {list(auth_method_dict.keys())}."
        )

    if resource_type is None:
        # No resource type was specified, so no resource type
        # specification can be returned.
        return auth_method_spec, None

    # Verify the resource type
    resource_type_dict = self.resource_type_dict
    if resource_type in resource_type_dict:
        resource_type_spec = resource_type_dict[resource_type]
    else:
        raise KeyError(
            f"connector type '{self.connector_type}' does not support "
            f"resource type '{resource_type}'. Supported resource types "
            f"are: {list(resource_type_dict.keys())}."
        )

    if auth_method not in resource_type_spec.auth_methods:
        raise KeyError(
            f"the '{self.connector_type}' connector type does not support "
            f"the '{auth_method}' authentication method for the "
            f"'{resource_type}' resource type. Supported authentication "
            f"methods are: {resource_type_spec.auth_methods}."
        )

    return auth_method_spec, resource_type_spec

`set_connector_class(connector_class)`

Set the service connector class.

Parameters:

Name	Type	Description	Default
`connector_class`	`Type[ServiceConnector]`	The service connector class.	required

Source code in src/zenml/models/v2/misc/service_connector_type.py

def set_connector_class(
    self, connector_class: Type["ServiceConnector"]
) -> None:
    """Set the service connector class.

    Args:
        connector_class: The service connector class.
    """
    self._connector_class = connector_class

`validate_auth_methods(values)` `classmethod`

Validate that the authentication methods are unique.

Parameters:

Name	Type	Description	Default
`values`	`List[AuthenticationMethodModel]`	The list of authentication methods.	required

Returns:

Type	Description
`List[AuthenticationMethodModel]`	The list of authentication methods.

Raises:

Type	Description
`ValueError`	If two or more authentication method specifications share the same authentication method value.

Source code in src/zenml/models/v2/misc/service_connector_type.py

@field_validator("auth_methods")
@classmethod
def validate_auth_methods(
    cls, values: List[AuthenticationMethodModel]
) -> List[AuthenticationMethodModel]:
    """Validate that the authentication methods are unique.

    Args:
        values: The list of authentication methods.

    Returns:
        The list of authentication methods.

    Raises:
        ValueError: If two or more authentication method specifications
            share the same authentication method value.
    """
    # Gather all auth methods from the list of auth method
    # specifications.
    auth_methods = [a.auth_method for a in values]
    if len(auth_methods) != len(set(auth_methods)):
        raise ValueError(
            "Two or more authentication method specifications must not "
            "share the same authentication method value."
        )

    return values

`validate_resource_types(values)` `classmethod`

Validate that the resource types are unique.

Parameters:

Name	Type	Description	Default
`values`	`List[ResourceTypeModel]`	The list of resource types.	required

Returns:

Type	Description
`List[ResourceTypeModel]`	The list of resource types.

Raises:

Type	Description
`ValueError`	If two or more resource type specifications list the same resource type.

Source code in src/zenml/models/v2/misc/service_connector_type.py

@field_validator("resource_types")
@classmethod
def validate_resource_types(
    cls, values: List[ResourceTypeModel]
) -> List[ResourceTypeModel]:
    """Validate that the resource types are unique.

    Args:
        values: The list of resource types.

    Returns:
        The list of resource types.

    Raises:
        ValueError: If two or more resource type specifications list the
            same resource type.
    """
    # Gather all resource types from the list of resource type
    # specifications.
    resource_types = [r.resource_type for r in values]
    if len(resource_types) != len(set(resource_types)):
        raise ValueError(
            "Two or more resource type specifications must not list "
            "the same resource type."
        )

    return values

`ServiceConnectorTypedResourcesModel`

Bases: BaseModel

Service connector typed resources list.

Lists the resource instances that a service connector can provide access to.

Source code in src/zenml/models/v2/misc/service_connector_type.py

class ServiceConnectorTypedResourcesModel(BaseModel):
    """Service connector typed resources list.

    Lists the resource instances that a service connector can provide
    access to.
    """

    resource_type: str = Field(
        title="The type of resource that the service connector instance can "
        "be used to access.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    resource_ids: Optional[List[str]] = Field(
        default=None,
        title="The resource IDs of all resource instances that the service "
        "connector instance can be used to access. Omitted (set to None) for "
        "multi-type service connectors that didn't explicitly request to "
        "fetch resources for all resource types. Also omitted if an error "
        "occurred while listing the resource instances or if no resources are "
        "listed due to authorization issues or lack of permissions (in both "
        "cases the 'error' field is set to an error message). For resource "
        "types that do not support multiple instances, a single resource ID is "
        "listed.",
    )

    error: Optional[str] = Field(
        default=None,
        title="An error message describing why the service connector instance "
        "could not list the resources that it is configured to access.",
    )

`ServiceConnectorUpdate`

Bases: BaseUpdate

Model used for service connector updates.

Most fields in the update model are optional and will not be updated if omitted. However, the following fields are "special" and leaving them out will also cause the corresponding value to be removed from the service connector in the database:

the resource_id field
the expiration_seconds field

In addition to the above exceptions, the following rules apply:

the configuration and secrets fields together represent a full valid configuration update, not just a partial update. If either is set (i.e. not None) in the update, their values are merged together and will replace the existing configuration and secrets values.
the secret_id field value in the update is ignored, given that secrets are managed internally by the ZenML store.
the labels field is also a full labels update: if set (i.e. not None), all existing labels are removed and replaced by the new labels in the update.

NOTE: the attributes here override the ones in the base class, so they have a None default value.

Source code in src/zenml/models/v2/core/service_connector.py

class ServiceConnectorUpdate(BaseUpdate):
    """Model used for service connector updates.

    Most fields in the update model are optional and will not be updated if
    omitted. However, the following fields are "special" and leaving them out
    will also cause the corresponding value to be removed from the service
    connector in the database:

    * the `resource_id` field
    * the `expiration_seconds` field

    In addition to the above exceptions, the following rules apply:

    * the `configuration` and `secrets` fields together represent a full
    valid configuration update, not just a partial update. If either is
    set (i.e. not None) in the update, their values are merged together and
    will replace the existing configuration and secrets values.
    * the `secret_id` field value in the update is ignored, given that
    secrets are managed internally by the ZenML store.
    * the `labels` field is also a full labels update: if set (i.e. not
    `None`), all existing labels are removed and replaced by the new labels
    in the update.

    NOTE: the attributes here override the ones in the base class, so they
    have a None default value.
    """

    name: Optional[str] = Field(
        title="The service connector name.",
        max_length=STR_FIELD_MAX_LENGTH,
        default=None,
    )
    connector_type: Optional[Union[str, "ServiceConnectorTypeModel"]] = Field(
        title="The type of service connector.",
        default=None,
        union_mode="left_to_right",
    )
    description: Optional[str] = Field(
        title="The service connector instance description.",
        default=None,
    )
    auth_method: Optional[str] = Field(
        title="The authentication method that the connector instance uses to "
        "access the resources.",
        max_length=STR_FIELD_MAX_LENGTH,
        default=None,
    )
    resource_types: Optional[List[str]] = Field(
        title="The type(s) of resource that the connector instance can be used "
        "to gain access to.",
        default=None,
    )
    resource_id: Optional[str] = Field(
        title="Uniquely identifies a specific resource instance that the "
        "connector instance can be used to access. If omitted, the "
        "connector instance can be used to access any and all resource "
        "instances that the authentication method and resource type(s) "
        "allow.",
        max_length=STR_FIELD_MAX_LENGTH,
        default=None,
    )
    supports_instances: Optional[bool] = Field(
        title="Indicates whether the connector instance can be used to access "
        "multiple instances of the configured resource type.",
        default=None,
    )
    expires_at: Optional[datetime] = Field(
        title="Time when the authentication credentials configured for the "
        "connector expire. If omitted, the credentials do not expire.",
        default=None,
    )
    expires_skew_tolerance: Optional[int] = Field(
        title="The number of seconds of tolerance to apply when checking "
        "whether the authentication credentials configured for the "
        "connector have expired. If omitted, no tolerance is applied.",
        default=None,
    )
    expiration_seconds: Optional[int] = Field(
        title="The duration, in seconds, that the temporary credentials "
        "generated by this connector should remain valid. Only "
        "applicable for connectors and authentication methods that "
        "involve generating temporary credentials from the ones "
        "configured in the connector.",
        default=None,
    )
    configuration: Optional[Dict[str, Any]] = Field(
        title="The service connector configuration, not including secrets.",
        default=None,
    )
    secrets: Optional[Dict[str, Optional[PlainSerializedSecretStr]]] = Field(
        title="The service connector secrets.",
        default=None,
    )
    labels: Optional[Dict[str, str]] = Field(
        title="Service connector labels.",
        default=None,
    )

    # Analytics
    ANALYTICS_FIELDS: ClassVar[List[str]] = [
        "connector_type",
        "auth_method",
        "resource_types",
    ]

    def get_analytics_metadata(self) -> Dict[str, Any]:
        """Format the resource types in the analytics metadata.

        Returns:
            Dict of analytics metadata.
        """
        metadata = super().get_analytics_metadata()

        if self.resource_types is not None:
            if len(self.resource_types) == 1:
                metadata["resource_types"] = self.resource_types[0]
            else:
                metadata["resource_types"] = ", ".join(self.resource_types)

        if self.connector_type is not None:
            metadata["connector_type"] = self.type

        return metadata

    # Helper methods
    @property
    def type(self) -> Optional[str]:
        """Get the connector type.

        Returns:
            The connector type.
        """
        if self.connector_type is not None:
            if isinstance(self.connector_type, str):
                return self.connector_type
            return self.connector_type.connector_type
        return None

    def validate_and_configure_resources(
        self,
        connector_type: "ServiceConnectorTypeModel",
        resource_types: Optional[Union[str, List[str]]] = None,
        resource_id: Optional[str] = None,
        configuration: Optional[Dict[str, Any]] = None,
        secrets: Optional[Dict[str, Optional[SecretStr]]] = None,
    ) -> None:
        """Validate and configure the resources that the connector can be used to access.

        Args:
            connector_type: The connector type specification used to validate
                the connector configuration.
            resource_types: The type(s) of resource that the connector instance
                can be used to access. If omitted, a multi-type connector is
                configured.
            resource_id: Uniquely identifies a specific resource instance that
                the connector instance can be used to access.
            configuration: The connector configuration.
            secrets: The connector secrets.
        """
        _validate_and_configure_resources(
            connector=self,
            connector_type=connector_type,
            resource_types=resource_types,
            resource_id=resource_id,
            configuration=configuration,
            secrets=secrets,
        )

    def convert_to_request(self) -> "ServiceConnectorRequest":
        """Method to generate a service connector request object from self.

        For certain operations, the service connector update model need to
        adhere to the limitations set by the request model. In order to use
        update models in such situations, we need to be able to convert an
        update model into a request model.

        Returns:
            The equivalent request model

        Raises:
            RuntimeError: if the model can not be converted to a request model.
        """
        try:
            return ServiceConnectorRequest.model_validate(self.model_dump())
        except ValidationError as e:
            raise RuntimeError(
                "The service connector update model can not be converted into "
                f"an equivalent request model: {e}"
            )

`type` `property`

Get the connector type.

Returns:

Type	Description
`Optional[str]`	The connector type.

`convert_to_request()`

Method to generate a service connector request object from self.

For certain operations, the service connector update model need to adhere to the limitations set by the request model. In order to use update models in such situations, we need to be able to convert an update model into a request model.

Returns:

Type	Description
`ServiceConnectorRequest`	The equivalent request model

Raises:

Type	Description
`RuntimeError`	if the model can not be converted to a request model.

Source code in src/zenml/models/v2/core/service_connector.py

def convert_to_request(self) -> "ServiceConnectorRequest":
    """Method to generate a service connector request object from self.

    For certain operations, the service connector update model need to
    adhere to the limitations set by the request model. In order to use
    update models in such situations, we need to be able to convert an
    update model into a request model.

    Returns:
        The equivalent request model

    Raises:
        RuntimeError: if the model can not be converted to a request model.
    """
    try:
        return ServiceConnectorRequest.model_validate(self.model_dump())
    except ValidationError as e:
        raise RuntimeError(
            "The service connector update model can not be converted into "
            f"an equivalent request model: {e}"
        )

`get_analytics_metadata()`

Format the resource types in the analytics metadata.

Returns:

Type	Description
`Dict[str, Any]`	Dict of analytics metadata.

Source code in src/zenml/models/v2/core/service_connector.py

def get_analytics_metadata(self) -> Dict[str, Any]:
    """Format the resource types in the analytics metadata.

    Returns:
        Dict of analytics metadata.
    """
    metadata = super().get_analytics_metadata()

    if self.resource_types is not None:
        if len(self.resource_types) == 1:
            metadata["resource_types"] = self.resource_types[0]
        else:
            metadata["resource_types"] = ", ".join(self.resource_types)

    if self.connector_type is not None:
        metadata["connector_type"] = self.type

    return metadata

`validate_and_configure_resources(connector_type, resource_types=None, resource_id=None, configuration=None, secrets=None)`

Validate and configure the resources that the connector can be used to access.

Parameters:

Name	Type	Description	Default
`connector_type`	`ServiceConnectorTypeModel`	The connector type specification used to validate the connector configuration.	required
`resource_types`	`Optional[Union[str, List[str]]]`	The type(s) of resource that the connector instance can be used to access. If omitted, a multi-type connector is configured.	`None`
`resource_id`	`Optional[str]`	Uniquely identifies a specific resource instance that the connector instance can be used to access.	`None`
`configuration`	`Optional[Dict[str, Any]]`	The connector configuration.	`None`
`secrets`	`Optional[Dict[str, Optional[SecretStr]]]`	The connector secrets.	`None`

Source code in src/zenml/models/v2/core/service_connector.py

def validate_and_configure_resources(
    self,
    connector_type: "ServiceConnectorTypeModel",
    resource_types: Optional[Union[str, List[str]]] = None,
    resource_id: Optional[str] = None,
    configuration: Optional[Dict[str, Any]] = None,
    secrets: Optional[Dict[str, Optional[SecretStr]]] = None,
) -> None:
    """Validate and configure the resources that the connector can be used to access.

    Args:
        connector_type: The connector type specification used to validate
            the connector configuration.
        resource_types: The type(s) of resource that the connector instance
            can be used to access. If omitted, a multi-type connector is
            configured.
        resource_id: Uniquely identifies a specific resource instance that
            the connector instance can be used to access.
        configuration: The connector configuration.
        secrets: The connector secrets.
    """
    _validate_and_configure_resources(
        connector=self,
        connector_type=connector_type,
        resource_types=resource_types,
        resource_id=resource_id,
        configuration=configuration,
        secrets=secrets,
    )

`ServiceFilter`

Model to enable advanced filtering of services.

The Service needs additional scoping. As such the _scope_user field can be set to the user that is doing the filtering. The generate_filter() method of the baseclass is overwritten to include the scoping.

Source code in src/zenml/models/v2/core/service.py

class ServiceFilter(WorkspaceScopedFilter):
    """Model to enable advanced filtering of services.

    The Service needs additional scoping. As such the `_scope_user` field
    can be set to the user that is doing the filtering. The
    `generate_filter()` method of the baseclass is overwritten to include the
    scoping.
    """

    name: Optional[str] = Field(
        default=None,
        description="Name of the service. Use this to filter services by "
        "their name.",
    )
    type: Optional[str] = Field(
        default=None,
        description="Type of the service. Filter services by their type.",
    )
    flavor: Optional[str] = Field(
        default=None,
        description="Flavor of the service. Use this to filter services by "
        "their flavor.",
    )
    config: Optional[bytes] = Field(
        default=None,
        description="Config of the service. Use this to filter services by "
        "their config.",
    )
    pipeline_name: Optional[str] = Field(
        default=None,
        description="Pipeline name responsible for deploying the service",
    )
    pipeline_step_name: Optional[str] = Field(
        default=None,
        description="Pipeline step name responsible for deploying the service",
    )
    running: Optional[bool] = Field(
        default=None, description="Whether the service is running"
    )
    model_version_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="By the model version this service is attached to.",
        union_mode="left_to_right",
    )
    pipeline_run_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="By the pipeline run this service is attached to.",
        union_mode="left_to_right",
    )

    # TODO: In Pydantic v2, the `model_` is a protected namespaces for all
    #  fields defined under base models. If not handled, this raises a warning.
    #  It is possible to suppress this warning message with the following
    #  configuration, however the ultimate solution is to rename these fields.
    #  Even though they do not cause any problems right now, if we are not
    #  careful we might overwrite some fields protected by pydantic.
    model_config = ConfigDict(protected_namespaces=())

    def set_type(self, type: str) -> None:
        """Set the type of the service.

        Args:
            type: The type of the service.
        """
        self.type = type

    def set_flavor(self, flavor: str) -> None:
        """Set the flavor of the service.

        Args:
            flavor: The flavor of the service.
        """
        self.flavor = flavor

    # Artifact name and type are not DB fields and need to be handled separately
    FILTER_EXCLUDE_FIELDS = [
        *WorkspaceScopedFilter.FILTER_EXCLUDE_FIELDS,
        "flavor",
        "type",
        "pipeline_step_name",
        "running",
        "pipeline_name",
        "config",
    ]
    CLI_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        *WorkspaceScopedFilter.CLI_EXCLUDE_FIELDS,
        "flavor",
        "type",
        "pipeline_step_name",
        "running",
        "pipeline_name",
    ]

    def generate_filter(
        self, table: Type["AnySchema"]
    ) -> Union["ColumnElement[bool]"]:
        """Generate the filter for the query.

        Services can be scoped by type to narrow the search.

        Args:
            table: The Table that is being queried from.

        Returns:
            The filter expression for the query.
        """
        from sqlmodel import and_

        base_filter = super().generate_filter(table)

        if self.type:
            type_filter = getattr(table, "type") == self.type
            base_filter = and_(base_filter, type_filter)

        if self.flavor:
            flavor_filter = getattr(table, "flavor") == self.flavor
            base_filter = and_(base_filter, flavor_filter)

        if self.pipeline_name:
            pipeline_name_filter = (
                getattr(table, "pipeline_name") == self.pipeline_name
            )
            base_filter = and_(base_filter, pipeline_name_filter)

        if self.pipeline_step_name:
            pipeline_step_name_filter = (
                getattr(table, "pipeline_step_name") == self.pipeline_step_name
            )
            base_filter = and_(base_filter, pipeline_step_name_filter)

        return base_filter

`generate_filter(table)`

Generate the filter for the query.

Services can be scoped by type to narrow the search.

Parameters:

Name	Type	Description	Default
`table`	`Type[AnySchema]`	The Table that is being queried from.	required

Returns:

Type	Description
`Union[ColumnElement[bool]]`	The filter expression for the query.

Source code in src/zenml/models/v2/core/service.py

def generate_filter(
    self, table: Type["AnySchema"]
) -> Union["ColumnElement[bool]"]:
    """Generate the filter for the query.

    Services can be scoped by type to narrow the search.

    Args:
        table: The Table that is being queried from.

    Returns:
        The filter expression for the query.
    """
    from sqlmodel import and_

    base_filter = super().generate_filter(table)

    if self.type:
        type_filter = getattr(table, "type") == self.type
        base_filter = and_(base_filter, type_filter)

    if self.flavor:
        flavor_filter = getattr(table, "flavor") == self.flavor
        base_filter = and_(base_filter, flavor_filter)

    if self.pipeline_name:
        pipeline_name_filter = (
            getattr(table, "pipeline_name") == self.pipeline_name
        )
        base_filter = and_(base_filter, pipeline_name_filter)

    if self.pipeline_step_name:
        pipeline_step_name_filter = (
            getattr(table, "pipeline_step_name") == self.pipeline_step_name
        )
        base_filter = and_(base_filter, pipeline_step_name_filter)

    return base_filter

`set_flavor(flavor)`

Set the flavor of the service.

Parameters:

Name	Type	Description	Default
`flavor`	`str`	The flavor of the service.	required

Source code in src/zenml/models/v2/core/service.py

def set_flavor(self, flavor: str) -> None:
    """Set the flavor of the service.

    Args:
        flavor: The flavor of the service.
    """
    self.flavor = flavor

`set_type(type)`

Set the type of the service.

Parameters:

Name	Type	Description	Default
`type`	`str`	The type of the service.	required

Source code in src/zenml/models/v2/core/service.py

def set_type(self, type: str) -> None:
    """Set the type of the service.

    Args:
        type: The type of the service.
    """
    self.type = type

`ServiceRequest`

Request model for services.

Source code in src/zenml/models/v2/core/service.py

class ServiceRequest(WorkspaceScopedRequest):
    """Request model for services."""

    name: str = Field(
        title="The name of the service.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    service_type: ServiceType = Field(
        title="The type of the service.",
    )
    service_source: Optional[str] = Field(
        title="The class of the service.",
        description="The fully qualified class name of the service "
        "implementation.",
        default=None,
    )
    admin_state: Optional[ServiceState] = Field(
        title="The admin state of the service.",
        description="The administrative state of the service, e.g., ACTIVE, "
        "INACTIVE.",
        default=None,
    )
    config: Dict[str, Any] = Field(
        title="The service config.",
        description="A dictionary containing configuration parameters for the "
        "service.",
    )
    labels: Optional[Dict[str, str]] = Field(
        default=None,
        title="The service labels.",
    )
    status: Optional[Dict[str, Any]] = Field(
        default=None,
        title="The status of the service.",
    )
    endpoint: Optional[Dict[str, Any]] = Field(
        default=None,
        title="The service endpoint.",
    )
    prediction_url: Optional[str] = Field(
        default=None,
        title="The service endpoint URL.",
    )
    health_check_url: Optional[str] = Field(
        default=None,
        title="The service health check URL.",
    )
    model_version_id: Optional[UUID] = Field(
        default=None,
        title="The model version id linked to the service.",
    )
    pipeline_run_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="By the event source this trigger is attached to.",
        union_mode="left_to_right",
    )

    # TODO: In Pydantic v2, the `model_` is a protected namespaces for all
    #  fields defined under base models. If not handled, this raises a warning.
    #  It is possible to suppress this warning message with the following
    #  configuration, however the ultimate solution is to rename these fields.
    #  Even though they do not cause any problems right now, if we are not
    #  careful we might overwrite some fields protected by pydantic.
    model_config = ConfigDict(protected_namespaces=())

`ServiceResponse`

Bases: WorkspaceScopedResponse[ServiceResponseBody, ServiceResponseMetadata, ServiceResponseResources]

Response model for services.

Source code in src/zenml/models/v2/core/service.py

class ServiceResponse(
    WorkspaceScopedResponse[
        ServiceResponseBody, ServiceResponseMetadata, ServiceResponseResources
    ]
):
    """Response model for services."""

    name: str = Field(
        title="The name of the service.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    def get_hydrated_version(self) -> "ServiceResponse":
        """Get the hydrated version of this artifact.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_service(self.id)

    # Body and metadata properties

    @property
    def service_type(self) -> ServiceType:
        """The `service_type` property.

        Returns:
            the value of the property.
        """
        return self.get_body().service_type

    @property
    def labels(self) -> Optional[Dict[str, str]]:
        """The `labels` property.

        Returns:
            the value of the property.
        """
        return self.get_body().labels

    @property
    def service_source(self) -> Optional[str]:
        """The `service_source` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().service_source

    @property
    def config(self) -> Dict[str, Any]:
        """The `config` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().config

    @property
    def status(self) -> Optional[Dict[str, Any]]:
        """The `status` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().status

    @property
    def endpoint(self) -> Optional[Dict[str, Any]]:
        """The `endpoint` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().endpoint

    @property
    def created(self) -> datetime:
        """The `created` property.

        Returns:
            the value of the property.
        """
        return self.get_body().created

    @property
    def updated(self) -> datetime:
        """The `updated` property.

        Returns:
            the value of the property.
        """
        return self.get_body().updated

    @property
    def admin_state(self) -> Optional[ServiceState]:
        """The `admin_state` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().admin_state

    @property
    def prediction_url(self) -> Optional[str]:
        """The `prediction_url` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().prediction_url

    @property
    def health_check_url(self) -> Optional[str]:
        """The `health_check_url` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().health_check_url

    @property
    def state(self) -> Optional[ServiceState]:
        """The `state` property.

        Returns:
            the value of the property.
        """
        return self.get_body().state

`admin_state` `property`

The admin_state property.

Returns:

Type	Description
`Optional[ServiceState]`	the value of the property.

`config` `property`

The config property.

Returns:

Type	Description
`Dict[str, Any]`	the value of the property.

`created` `property`

The created property.

Returns:

Type	Description
`datetime`	the value of the property.

`endpoint` `property`

The endpoint property.

Returns:

Type	Description
`Optional[Dict[str, Any]]`	the value of the property.

`health_check_url` `property`

The health_check_url property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`labels` `property`

The labels property.

Returns:

Type	Description
`Optional[Dict[str, str]]`	the value of the property.

`prediction_url` `property`

The prediction_url property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`service_source` `property`

The service_source property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`service_type` `property`

The service_type property.

Returns:

Type	Description
`ServiceType`	the value of the property.

`state` `property`

The state property.

Returns:

Type	Description
`Optional[ServiceState]`	the value of the property.

`status` `property`

The status property.

Returns:

Type	Description
`Optional[Dict[str, Any]]`	the value of the property.

`updated` `property`

The updated property.

Returns:

Type	Description
`datetime`	the value of the property.

`get_hydrated_version()`

Get the hydrated version of this artifact.

Returns:

Type	Description
`ServiceResponse`	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/service.py

def get_hydrated_version(self) -> "ServiceResponse":
    """Get the hydrated version of this artifact.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_service(self.id)

`ServiceResponseBody`

Response body for services.

Source code in src/zenml/models/v2/core/service.py

class ServiceResponseBody(WorkspaceScopedResponseBody):
    """Response body for services."""

    service_type: ServiceType = Field(
        title="The type of the service.",
    )
    labels: Optional[Dict[str, str]] = Field(
        default=None,
        title="The service labels.",
    )
    created: datetime = Field(
        title="The timestamp when this component was created."
    )
    updated: datetime = Field(
        title="The timestamp when this component was last updated.",
    )
    state: Optional[ServiceState] = Field(
        default=None,
        title="The current state of the service.",
    )

`ServiceResponseMetadata`

Response metadata for services.

Source code in src/zenml/models/v2/core/service.py

class ServiceResponseMetadata(WorkspaceScopedResponseMetadata):
    """Response metadata for services."""

    service_source: Optional[str] = Field(
        title="The class of the service.",
    )
    admin_state: Optional[ServiceState] = Field(
        title="The admin state of the service.",
    )
    config: Dict[str, Any] = Field(
        title="The service config.",
    )
    status: Optional[Dict[str, Any]] = Field(
        title="The status of the service.",
    )
    endpoint: Optional[Dict[str, Any]] = Field(
        default=None,
        title="The service endpoint.",
    )
    prediction_url: Optional[str] = Field(
        default=None,
        title="The service endpoint URL.",
    )
    health_check_url: Optional[str] = Field(
        default=None,
        title="The service health check URL.",
    )

`ServiceResponseResources`

Class for all resource models associated with the service entity.

Source code in src/zenml/models/v2/core/service.py

class ServiceResponseResources(WorkspaceScopedResponseResources):
    """Class for all resource models associated with the service entity."""

`ServiceUpdate`

Bases: BaseModel

Update model for stack components.

Source code in src/zenml/models/v2/core/service.py

class ServiceUpdate(BaseModel):
    """Update model for stack components."""

    name: Optional[str] = Field(
        None,
        title="The name of the service.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    admin_state: Optional[ServiceState] = Field(
        None,
        title="The admin state of the service.",
        description="The administrative state of the service, e.g., ACTIVE, "
        "INACTIVE.",
    )
    service_source: Optional[str] = Field(
        None,
        title="The class of the service.",
        description="The fully qualified class name of the service "
        "implementation.",
    )
    status: Optional[Dict[str, Any]] = Field(
        None,
        title="The status of the service.",
    )
    endpoint: Optional[Dict[str, Any]] = Field(
        None,
        title="The service endpoint.",
    )
    prediction_url: Optional[str] = Field(
        None,
        title="The service endpoint URL.",
    )
    health_check_url: Optional[str] = Field(
        None,
        title="The service health check URL.",
    )
    labels: Optional[Dict[str, str]] = Field(
        default=None,
        title="The service labels.",
    )
    model_version_id: Optional[UUID] = Field(
        default=None,
        title="The model version id linked to the service.",
    )

    # TODO: In Pydantic v2, the `model_` is a protected namespaces for all
    #  fields defined under base models. If not handled, this raises a warning.
    #  It is possible to suppress this warning message with the following
    #  configuration, however the ultimate solution is to rename these fields.
    #  Even though they do not cause any problems right now, if we are not
    #  careful we might overwrite some fields protected by pydantic.
    model_config = ConfigDict(protected_namespaces=())

`StackDeploymentConfig`

Bases: BaseModel

Configuration about a stack deployment.

Source code in src/zenml/models/v2/misc/stack_deployment.py

class StackDeploymentConfig(BaseModel):
    """Configuration about a stack deployment."""

    deployment_url: str = Field(
        title="The cloud provider console URL where the stack will be deployed.",
    )
    deployment_url_text: str = Field(
        title="A textual description for the cloud provider console URL.",
    )
    configuration: Optional[str] = Field(
        default=None,
        title="Configuration for the stack deployment that the user must "
        "manually configure into the cloud provider console.",
    )
    instructions: Optional[str] = Field(
        default=None,
        title="Instructions for deploying the stack.",
    )

`StackDeploymentInfo`

Bases: BaseModel

Information about a stack deployment.

Source code in src/zenml/models/v2/misc/stack_deployment.py

class StackDeploymentInfo(BaseModel):
    """Information about a stack deployment."""

    provider: StackDeploymentProvider = Field(
        title="The provider of the stack deployment."
    )
    description: str = Field(
        title="The description of the stack deployment.",
        description="The description of the stack deployment.",
    )
    instructions: str = Field(
        title="The instructions for deploying the stack.",
        description="The instructions for deploying the stack.",
    )
    post_deploy_instructions: str = Field(
        title="The instructions for post-deployment.",
        description="The instructions for post-deployment.",
    )
    integrations: List[str] = Field(
        title="ZenML integrations required for the stack.",
        description="The list of ZenML integrations that need to be installed "
        "for the stack to be usable.",
    )
    permissions: Dict[str, List[str]] = Field(
        title="The permissions granted to ZenML to access the cloud resources.",
        description="The permissions granted to ZenML to access the cloud "
        "resources, as a dictionary grouping permissions by resource.",
    )
    locations: Dict[str, str] = Field(
        title="The locations where the stack can be deployed.",
        description="The locations where the stack can be deployed, as a "
        "dictionary mapping location names to descriptions.",
    )
    skypilot_default_regions: Dict[str, str] = Field(
        title="The locations where the Skypilot clusters can be deployed by default.",
        description="The locations where the Skypilot clusters can be deployed by default, as a "
        "dictionary mapping location names to descriptions.",
    )

`StackFilter`

Model to enable advanced filtering of all StackModels.

The Stack Model needs additional scoping. As such the _scope_user field can be set to the user that is doing the filtering. The generate_filter() method of the baseclass is overwritten to include the scoping.

Source code in src/zenml/models/v2/core/stack.py

class StackFilter(WorkspaceScopedFilter):
    """Model to enable advanced filtering of all StackModels.

    The Stack Model needs additional scoping. As such the `_scope_user` field
    can be set to the user that is doing the filtering. The
    `generate_filter()` method of the baseclass is overwritten to include the
    scoping.
    """

    FILTER_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        *WorkspaceScopedFilter.FILTER_EXCLUDE_FIELDS,
        "component_id",
        "component",
    ]

    name: Optional[str] = Field(
        default=None,
        description="Name of the stack",
    )
    description: Optional[str] = Field(
        default=None, description="Description of the stack"
    )
    component_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Component in the stack",
        union_mode="left_to_right",
    )
    component: Optional[Union[UUID, str]] = Field(
        default=None, description="Name/ID of a component in the stack."
    )

    def get_custom_filters(
        self, table: Type["AnySchema"]
    ) -> List["ColumnElement[bool]"]:
        """Get custom filters.

        Args:
            table: The query table.

        Returns:
            A list of custom filters.
        """
        custom_filters = super().get_custom_filters(table)

        from zenml.zen_stores.schemas import (
            StackComponentSchema,
            StackCompositionSchema,
            StackSchema,
        )

        if self.component_id:
            component_id_filter = and_(
                StackCompositionSchema.stack_id == StackSchema.id,
                StackCompositionSchema.component_id == self.component_id,
            )
            custom_filters.append(component_id_filter)

        if self.component:
            component_filter = and_(
                StackCompositionSchema.stack_id == StackSchema.id,
                StackCompositionSchema.component_id == StackComponentSchema.id,
                self.generate_name_or_id_query_conditions(
                    value=self.component,
                    table=StackComponentSchema,
                ),
            )
            custom_filters.append(component_filter)

        return custom_filters

`get_custom_filters(table)`

Get custom filters.

Parameters:

Name	Type	Description	Default
`table`	`Type[AnySchema]`	The query table.	required

Returns:

Type	Description
`List[ColumnElement[bool]]`	A list of custom filters.

Source code in src/zenml/models/v2/core/stack.py

def get_custom_filters(
    self, table: Type["AnySchema"]
) -> List["ColumnElement[bool]"]:
    """Get custom filters.

    Args:
        table: The query table.

    Returns:
        A list of custom filters.
    """
    custom_filters = super().get_custom_filters(table)

    from zenml.zen_stores.schemas import (
        StackComponentSchema,
        StackCompositionSchema,
        StackSchema,
    )

    if self.component_id:
        component_id_filter = and_(
            StackCompositionSchema.stack_id == StackSchema.id,
            StackCompositionSchema.component_id == self.component_id,
        )
        custom_filters.append(component_id_filter)

    if self.component:
        component_filter = and_(
            StackCompositionSchema.stack_id == StackSchema.id,
            StackCompositionSchema.component_id == StackComponentSchema.id,
            self.generate_name_or_id_query_conditions(
                value=self.component,
                table=StackComponentSchema,
            ),
        )
        custom_filters.append(component_filter)

    return custom_filters

`StackRequest`

Bases: BaseRequest

Request model for a stack.

Source code in src/zenml/models/v2/core/stack.py

class StackRequest(BaseRequest):
    """Request model for a stack."""

    user: Optional[UUID] = None
    workspace: Optional[UUID] = None

    name: str = Field(
        title="The name of the stack.", max_length=STR_FIELD_MAX_LENGTH
    )
    description: str = Field(
        default="",
        title="The description of the stack",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    stack_spec_path: Optional[str] = Field(
        default=None,
        title="The path to the stack spec used for mlstacks deployments.",
    )
    components: Dict[StackComponentType, List[Union[UUID, ComponentInfo]]] = (
        Field(
            title="The mapping for the components of the full stack registration.",
            description="The mapping from component types to either UUIDs of "
            "existing components or request information for brand new "
            "components.",
        )
    )
    labels: Optional[Dict[str, Any]] = Field(
        default=None,
        title="The stack labels.",
    )
    service_connectors: List[Union[UUID, ServiceConnectorInfo]] = Field(
        default=[],
        title="The service connectors dictionary for the full stack "
        "registration.",
        description="The UUID of an already existing service connector or "
        "request information to create a service connector from "
        "scratch.",
    )

    @property
    def is_valid(self) -> bool:
        """Check if the stack is valid.

        Returns:
            True if the stack is valid, False otherwise.
        """
        if not self.components:
            return False
        return (
            StackComponentType.ARTIFACT_STORE in self.components
            and StackComponentType.ORCHESTRATOR in self.components
        )

    @model_validator(mode="after")
    def _validate_indexes_in_components(self) -> "StackRequest":
        for components in self.components.values():
            for component in components:
                if isinstance(component, ComponentInfo):
                    if component.service_connector_index is not None:
                        if (
                            component.service_connector_index < 0
                            or component.service_connector_index
                            >= len(self.service_connectors)
                        ):
                            raise ValueError(
                                f"Service connector index "
                                f"{component.service_connector_index} "
                                "is out of range. Please provide a valid index "
                                "referring to the position in the list of service "
                                "connectors."
                            )
        return self

`is_valid` `property`

Check if the stack is valid.

Returns:

Type	Description
`bool`	True if the stack is valid, False otherwise.

`StackResponse`

Bases: WorkspaceScopedResponse[StackResponseBody, StackResponseMetadata, StackResponseResources]

Response model for stacks.

Source code in src/zenml/models/v2/core/stack.py

class StackResponse(
    WorkspaceScopedResponse[
        StackResponseBody, StackResponseMetadata, StackResponseResources
    ]
):
    """Response model for stacks."""

    name: str = Field(
        title="The name of the stack.", max_length=STR_FIELD_MAX_LENGTH
    )

    def get_hydrated_version(self) -> "StackResponse":
        """Get the hydrated version of this stack.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_stack(self.id)

    # Helper methods
    @property
    def is_valid(self) -> bool:
        """Check if the stack is valid.

        Returns:
            True if the stack is valid, False otherwise.
        """
        return (
            StackComponentType.ARTIFACT_STORE in self.components
            and StackComponentType.ORCHESTRATOR in self.components
        )

    def to_yaml(self) -> Dict[str, Any]:
        """Create yaml representation of the Stack Model.

        Returns:
            The yaml representation of the Stack Model.
        """
        component_data = {}
        for component_type, components_list in self.components.items():
            component = components_list[0]
            component_dict = dict(
                name=component.name,
                type=str(component.type),
                flavor=component.flavor_name,
            )
            configuration = json.loads(
                component.get_metadata().model_dump_json(
                    include={"configuration"}
                )
            )
            component_dict.update(configuration)

            component_data[component_type.value] = component_dict

        # write zenml version and stack dict to YAML
        yaml_data = {
            "stack_name": self.name,
            "components": component_data,
        }

        return yaml_data

    # Analytics
    def get_analytics_metadata(self) -> Dict[str, Any]:
        """Add the stack components to the stack analytics metadata.

        Returns:
            Dict of analytics metadata.
        """
        metadata = super().get_analytics_metadata()
        metadata.update(
            {ct: c[0].flavor_name for ct, c in self.components.items()}
        )

        if self.labels is not None:
            metadata.update(
                {
                    label[6:]: value
                    for label, value in self.labels.items()
                    if label.startswith("zenml:")
                }
            )
        return metadata

    @property
    def description(self) -> Optional[str]:
        """The `description` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().description

    @property
    def stack_spec_path(self) -> Optional[str]:
        """The `stack_spec_path` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().stack_spec_path

    @property
    def components(
        self,
    ) -> Dict[StackComponentType, List["ComponentResponse"]]:
        """The `components` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().components

    @property
    def labels(self) -> Optional[Dict[str, Any]]:
        """The `labels` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().labels

`components` `property`

The components property.

Returns:

Type	Description
`Dict[StackComponentType, List[ComponentResponse]]`	the value of the property.

`description` `property`

The description property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`is_valid` `property`

Check if the stack is valid.

Returns:

Type	Description
`bool`	True if the stack is valid, False otherwise.

`labels` `property`

The labels property.

Returns:

Type	Description
`Optional[Dict[str, Any]]`	the value of the property.

`stack_spec_path` `property`

The stack_spec_path property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`get_analytics_metadata()`

Add the stack components to the stack analytics metadata.

Returns:

Type	Description
`Dict[str, Any]`	Dict of analytics metadata.

Source code in src/zenml/models/v2/core/stack.py

def get_analytics_metadata(self) -> Dict[str, Any]:
    """Add the stack components to the stack analytics metadata.

    Returns:
        Dict of analytics metadata.
    """
    metadata = super().get_analytics_metadata()
    metadata.update(
        {ct: c[0].flavor_name for ct, c in self.components.items()}
    )

    if self.labels is not None:
        metadata.update(
            {
                label[6:]: value
                for label, value in self.labels.items()
                if label.startswith("zenml:")
            }
        )
    return metadata

`get_hydrated_version()`

Get the hydrated version of this stack.

Returns:

Type	Description
`StackResponse`	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/stack.py

def get_hydrated_version(self) -> "StackResponse":
    """Get the hydrated version of this stack.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_stack(self.id)

`to_yaml()`

Create yaml representation of the Stack Model.

Returns:

Type	Description
`Dict[str, Any]`	The yaml representation of the Stack Model.

Source code in src/zenml/models/v2/core/stack.py

def to_yaml(self) -> Dict[str, Any]:
    """Create yaml representation of the Stack Model.

    Returns:
        The yaml representation of the Stack Model.
    """
    component_data = {}
    for component_type, components_list in self.components.items():
        component = components_list[0]
        component_dict = dict(
            name=component.name,
            type=str(component.type),
            flavor=component.flavor_name,
        )
        configuration = json.loads(
            component.get_metadata().model_dump_json(
                include={"configuration"}
            )
        )
        component_dict.update(configuration)

        component_data[component_type.value] = component_dict

    # write zenml version and stack dict to YAML
    yaml_data = {
        "stack_name": self.name,
        "components": component_data,
    }

    return yaml_data

`StackResponseBody`

Response body for stacks.

Source code in src/zenml/models/v2/core/stack.py

class StackResponseBody(WorkspaceScopedResponseBody):
    """Response body for stacks."""

`StackResponseMetadata`

Response metadata for stacks.

Source code in src/zenml/models/v2/core/stack.py

class StackResponseMetadata(WorkspaceScopedResponseMetadata):
    """Response metadata for stacks."""

    components: Dict[StackComponentType, List["ComponentResponse"]] = Field(
        title="A mapping of stack component types to the actual"
        "instances of components of this type."
    )
    description: Optional[str] = Field(
        default="",
        title="The description of the stack",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    stack_spec_path: Optional[str] = Field(
        default=None,
        title="The path to the stack spec used for mlstacks deployments.",
    )
    labels: Optional[Dict[str, Any]] = Field(
        default=None,
        title="The stack labels.",
    )

`StackUpdate`

Bases: BaseUpdate

Update model for stacks.

Source code in src/zenml/models/v2/core/stack.py

class StackUpdate(BaseUpdate):
    """Update model for stacks."""

    name: Optional[str] = Field(
        title="The name of the stack.",
        max_length=STR_FIELD_MAX_LENGTH,
        default=None,
    )
    description: Optional[str] = Field(
        title="The description of the stack",
        max_length=STR_FIELD_MAX_LENGTH,
        default=None,
    )
    stack_spec_path: Optional[str] = Field(
        title="The path to the stack spec used for mlstacks deployments.",
        default=None,
    )
    components: Optional[Dict[StackComponentType, List[UUID]]] = Field(
        title="A mapping of stack component types to the actual"
        "instances of components of this type.",
        default=None,
    )
    labels: Optional[Dict[str, Any]] = Field(
        default=None,
        title="The stack labels.",
    )

`StepRunFilter`

Model to enable advanced filtering of step runs.

Source code in src/zenml/models/v2/core/step_run.py

class StepRunFilter(WorkspaceScopedFilter):
    """Model to enable advanced filtering of step runs."""

    FILTER_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        *WorkspaceScopedFilter.FILTER_EXCLUDE_FIELDS,
        "model",
        "run_metadata",
    ]

    name: Optional[str] = Field(
        default=None,
        description="Name of the step run",
    )
    code_hash: Optional[str] = Field(
        default=None,
        description="Code hash for this step run",
    )
    cache_key: Optional[str] = Field(
        default=None,
        description="Cache key for this step run",
    )
    status: Optional[str] = Field(
        default=None,
        description="Status of the Step Run",
    )
    start_time: Optional[Union[datetime, str]] = Field(
        default=None,
        description="Start time for this run",
        union_mode="left_to_right",
    )
    end_time: Optional[Union[datetime, str]] = Field(
        default=None,
        description="End time for this run",
        union_mode="left_to_right",
    )
    pipeline_run_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Pipeline run of this step run",
        union_mode="left_to_right",
    )
    deployment_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Deployment of this step run",
        union_mode="left_to_right",
    )
    original_step_run_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Original id for this step run",
        union_mode="left_to_right",
    )
    model_version_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Model version associated with the step run.",
        union_mode="left_to_right",
    )
    model: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Name/ID of the model associated with the step run.",
    )
    run_metadata: Optional[Dict[str, Any]] = Field(
        default=None,
        description="The run_metadata to filter the step runs by.",
    )
    model_config = ConfigDict(protected_namespaces=())

    def get_custom_filters(
        self, table: Type["AnySchema"]
    ) -> List["ColumnElement[bool]"]:
        """Get custom filters.

        Args:
            table: The query table.

        Returns:
            A list of custom filters.
        """
        custom_filters = super().get_custom_filters(table)

        from sqlmodel import and_

        from zenml.zen_stores.schemas import (
            ModelSchema,
            ModelVersionSchema,
            RunMetadataResourceSchema,
            RunMetadataSchema,
            StepRunSchema,
        )

        if self.model:
            model_filter = and_(
                StepRunSchema.model_version_id == ModelVersionSchema.id,
                ModelVersionSchema.model_id == ModelSchema.id,
                self.generate_name_or_id_query_conditions(
                    value=self.model, table=ModelSchema
                ),
            )
            custom_filters.append(model_filter)
        if self.run_metadata is not None:
            from zenml.enums import MetadataResourceTypes

            for key, value in self.run_metadata.items():
                additional_filter = and_(
                    RunMetadataResourceSchema.resource_id == StepRunSchema.id,
                    RunMetadataResourceSchema.resource_type
                    == MetadataResourceTypes.STEP_RUN.value,
                    RunMetadataResourceSchema.run_metadata_id
                    == RunMetadataSchema.id,
                    self.generate_custom_query_conditions_for_column(
                        value=key,
                        table=RunMetadataSchema,
                        column="key",
                    ),
                    self.generate_custom_query_conditions_for_column(
                        value=value,
                        table=RunMetadataSchema,
                        column="value",
                        json_encode_value=True,
                    ),
                )
                custom_filters.append(additional_filter)

        return custom_filters

`get_custom_filters(table)`

Get custom filters.

Parameters:

Name	Type	Description	Default
`table`	`Type[AnySchema]`	The query table.	required

Returns:

Type	Description
`List[ColumnElement[bool]]`	A list of custom filters.

Source code in src/zenml/models/v2/core/step_run.py

def get_custom_filters(
    self, table: Type["AnySchema"]
) -> List["ColumnElement[bool]"]:
    """Get custom filters.

    Args:
        table: The query table.

    Returns:
        A list of custom filters.
    """
    custom_filters = super().get_custom_filters(table)

    from sqlmodel import and_

    from zenml.zen_stores.schemas import (
        ModelSchema,
        ModelVersionSchema,
        RunMetadataResourceSchema,
        RunMetadataSchema,
        StepRunSchema,
    )

    if self.model:
        model_filter = and_(
            StepRunSchema.model_version_id == ModelVersionSchema.id,
            ModelVersionSchema.model_id == ModelSchema.id,
            self.generate_name_or_id_query_conditions(
                value=self.model, table=ModelSchema
            ),
        )
        custom_filters.append(model_filter)
    if self.run_metadata is not None:
        from zenml.enums import MetadataResourceTypes

        for key, value in self.run_metadata.items():
            additional_filter = and_(
                RunMetadataResourceSchema.resource_id == StepRunSchema.id,
                RunMetadataResourceSchema.resource_type
                == MetadataResourceTypes.STEP_RUN.value,
                RunMetadataResourceSchema.run_metadata_id
                == RunMetadataSchema.id,
                self.generate_custom_query_conditions_for_column(
                    value=key,
                    table=RunMetadataSchema,
                    column="key",
                ),
                self.generate_custom_query_conditions_for_column(
                    value=value,
                    table=RunMetadataSchema,
                    column="value",
                    json_encode_value=True,
                ),
            )
            custom_filters.append(additional_filter)

    return custom_filters

`StepRunRequest`

Request model for step runs.

Source code in src/zenml/models/v2/core/step_run.py

class StepRunRequest(WorkspaceScopedRequest):
    """Request model for step runs."""

    name: str = Field(
        title="The name of the pipeline run step.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    start_time: Optional[datetime] = Field(
        title="The start time of the step run.",
        default=None,
    )
    end_time: Optional[datetime] = Field(
        title="The end time of the step run.",
        default=None,
    )
    status: ExecutionStatus = Field(title="The status of the step.")
    cache_key: Optional[str] = Field(
        title="The cache key of the step run.",
        default=None,
        max_length=STR_FIELD_MAX_LENGTH,
    )
    code_hash: Optional[str] = Field(
        title="The code hash of the step run.",
        default=None,
        max_length=STR_FIELD_MAX_LENGTH,
    )
    docstring: Optional[str] = Field(
        title="The docstring of the step function or class.",
        default=None,
        max_length=TEXT_FIELD_MAX_LENGTH,
    )
    source_code: Optional[str] = Field(
        title="The source code of the step function or class.",
        default=None,
        max_length=TEXT_FIELD_MAX_LENGTH,
    )
    pipeline_run_id: UUID = Field(
        title="The ID of the pipeline run that this step run belongs to.",
    )
    original_step_run_id: Optional[UUID] = Field(
        title="The ID of the original step run if this step was cached.",
        default=None,
    )
    parent_step_ids: List[UUID] = Field(
        title="The IDs of the parent steps of this step run.",
        default_factory=list,
    )
    inputs: Dict[str, UUID] = Field(
        title="The IDs of the input artifact versions of the step run.",
        default_factory=dict,
    )
    outputs: Dict[str, List[UUID]] = Field(
        title="The IDs of the output artifact versions of the step run.",
        default_factory=dict,
    )
    logs: Optional["LogsRequest"] = Field(
        title="Logs associated with this step run.",
        default=None,
    )
    deployment: UUID = Field(
        title="The deployment associated with the step run."
    )
    model_version_id: Optional[UUID] = Field(
        title="The ID of the model version that was "
        "configured by this step run explicitly.",
        default=None,
    )

    model_config = ConfigDict(protected_namespaces=())

`StepRunResponse`

Bases: WorkspaceScopedResponse[StepRunResponseBody, StepRunResponseMetadata, StepRunResponseResources]

Response model for step runs.

Source code in src/zenml/models/v2/core/step_run.py

class StepRunResponse(
    WorkspaceScopedResponse[
        StepRunResponseBody, StepRunResponseMetadata, StepRunResponseResources
    ]
):
    """Response model for step runs."""

    name: str = Field(
        title="The name of the pipeline run step.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    def get_hydrated_version(self) -> "StepRunResponse":
        """Get the hydrated version of this step run.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_run_step(self.id)

    # Helper properties
    @property
    def input(self) -> ArtifactVersionResponse:
        """Returns the input artifact that was used to run this step.

        Returns:
            The input artifact.

        Raises:
            ValueError: If there were zero or multiple inputs to this step.
        """
        if not self.inputs:
            raise ValueError(f"Step {self.name} has no inputs.")
        if len(self.inputs) > 1:
            raise ValueError(
                f"Step {self.name} has multiple inputs, so `Step.input` is "
                "ambiguous. Please use `Step.inputs` instead."
            )
        return next(iter(self.inputs.values()))

    @property
    def output(self) -> ArtifactVersionResponse:
        """Returns the output artifact that was written by this step.

        Returns:
            The output artifact.

        Raises:
            ValueError: If there were zero or multiple step outputs.
        """
        if not self.outputs:
            raise ValueError(f"Step {self.name} has no outputs.")
        if len(self.outputs) > 1 or (
            len(self.outputs) == 1
            and len(next(iter(self.outputs.values()))) > 1
        ):
            raise ValueError(
                f"Step {self.name} has multiple outputs, so `Step.output` is "
                "ambiguous. Please use `Step.outputs` instead."
            )
        return next(iter(self.outputs.values()))[0]

    # Body and metadata properties
    @property
    def status(self) -> ExecutionStatus:
        """The `status` property.

        Returns:
            the value of the property.
        """
        return self.get_body().status

    @property
    def inputs(self) -> Dict[str, StepRunInputResponse]:
        """The `inputs` property.

        Returns:
            the value of the property.
        """
        return self.get_body().inputs

    @property
    def outputs(self) -> Dict[str, List[ArtifactVersionResponse]]:
        """The `outputs` property.

        Returns:
            the value of the property.
        """
        return self.get_body().outputs

    @property
    def model_version_id(self) -> Optional[UUID]:
        """The `model_version_id` property.

        Returns:
            the value of the property.
        """
        return self.get_body().model_version_id

    @property
    def config(self) -> "StepConfiguration":
        """The `config` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().config

    @property
    def spec(self) -> "StepSpec":
        """The `spec` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().spec

    @property
    def cache_key(self) -> Optional[str]:
        """The `cache_key` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().cache_key

    @property
    def code_hash(self) -> Optional[str]:
        """The `code_hash` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().code_hash

    @property
    def docstring(self) -> Optional[str]:
        """The `docstring` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().docstring

    @property
    def source_code(self) -> Optional[str]:
        """The `source_code` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().source_code

    @property
    def start_time(self) -> Optional[datetime]:
        """The `start_time` property.

        Returns:
            the value of the property.
        """
        return self.get_body().start_time

    @property
    def end_time(self) -> Optional[datetime]:
        """The `end_time` property.

        Returns:
            the value of the property.
        """
        return self.get_body().end_time

    @property
    def logs(self) -> Optional["LogsResponse"]:
        """The `logs` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().logs

    @property
    def deployment_id(self) -> UUID:
        """The `deployment_id` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().deployment_id

    @property
    def pipeline_run_id(self) -> UUID:
        """The `pipeline_run_id` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().pipeline_run_id

    @property
    def original_step_run_id(self) -> Optional[UUID]:
        """The `original_step_run_id` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().original_step_run_id

    @property
    def parent_step_ids(self) -> List[UUID]:
        """The `parent_step_ids` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().parent_step_ids

    @property
    def run_metadata(self) -> Dict[str, MetadataType]:
        """The `run_metadata` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().run_metadata

    @property
    def model_version(self) -> Optional[ModelVersionResponse]:
        """The `model_version` property.

        Returns:
            the value of the property.
        """
        return self.get_resources().model_version

`cache_key` `property`

The cache_key property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`code_hash` `property`

The code_hash property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`config` `property`

The config property.

Returns:

Type	Description
`StepConfiguration`	the value of the property.

`deployment_id` `property`

The deployment_id property.

Returns:

Type	Description
`UUID`	the value of the property.

`docstring` `property`

The docstring property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`end_time` `property`

The end_time property.

Returns:

Type	Description
`Optional[datetime]`	the value of the property.

`input` `property`

Returns the input artifact that was used to run this step.

Returns:

Type	Description
`ArtifactVersionResponse`	The input artifact.

Raises:

Type	Description
`ValueError`	If there were zero or multiple inputs to this step.

`inputs` `property`

The inputs property.

Returns:

Type	Description
`Dict[str, StepRunInputResponse]`	the value of the property.

`logs` `property`

The logs property.

Returns:

Type	Description
`Optional[LogsResponse]`	the value of the property.

`model_version` `property`

The model_version property.

Returns:

Type	Description
`Optional[ModelVersionResponse]`	the value of the property.

`model_version_id` `property`

The model_version_id property.

Returns:

Type	Description
`Optional[UUID]`	the value of the property.

`original_step_run_id` `property`

The original_step_run_id property.

Returns:

Type	Description
`Optional[UUID]`	the value of the property.

`output` `property`

Returns the output artifact that was written by this step.

Returns:

Type	Description
`ArtifactVersionResponse`	The output artifact.

Raises:

Type	Description
`ValueError`	If there were zero or multiple step outputs.

`outputs` `property`

The outputs property.

Returns:

Type	Description
`Dict[str, List[ArtifactVersionResponse]]`	the value of the property.

`parent_step_ids` `property`

The parent_step_ids property.

Returns:

Type	Description
`List[UUID]`	the value of the property.

`pipeline_run_id` `property`

The pipeline_run_id property.

Returns:

Type	Description
`UUID`	the value of the property.

`run_metadata` `property`

The run_metadata property.

Returns:

Type	Description
`Dict[str, MetadataType]`	the value of the property.

`source_code` `property`

The source_code property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`spec` `property`

The spec property.

Returns:

Type	Description
`StepSpec`	the value of the property.

`start_time` `property`

The start_time property.

Returns:

Type	Description
`Optional[datetime]`	the value of the property.

`status` `property`

The status property.

Returns:

Type	Description
`ExecutionStatus`	the value of the property.

`get_hydrated_version()`

Get the hydrated version of this step run.

Returns:

Type	Description
`StepRunResponse`	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/step_run.py

def get_hydrated_version(self) -> "StepRunResponse":
    """Get the hydrated version of this step run.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_run_step(self.id)

`StepRunResponseBody`

Response body for step runs.

Source code in src/zenml/models/v2/core/step_run.py

class StepRunResponseBody(WorkspaceScopedResponseBody):
    """Response body for step runs."""

    status: ExecutionStatus = Field(title="The status of the step.")
    start_time: Optional[datetime] = Field(
        title="The start time of the step run.",
        default=None,
    )
    end_time: Optional[datetime] = Field(
        title="The end time of the step run.",
        default=None,
    )
    inputs: Dict[str, StepRunInputResponse] = Field(
        title="The input artifact versions of the step run.",
        default_factory=dict,
    )
    outputs: Dict[str, List[ArtifactVersionResponse]] = Field(
        title="The output artifact versions of the step run.",
        default_factory=dict,
    )
    model_version_id: Optional[UUID] = Field(
        title="The ID of the model version that was "
        "configured by this step run explicitly.",
        default=None,
    )
    model_config = ConfigDict(protected_namespaces=())

`StepRunResponseMetadata`

Response metadata for step runs.

Source code in src/zenml/models/v2/core/step_run.py

class StepRunResponseMetadata(WorkspaceScopedResponseMetadata):
    """Response metadata for step runs."""

    # Configuration
    config: "StepConfiguration" = Field(title="The configuration of the step.")
    spec: "StepSpec" = Field(title="The spec of the step.")

    # Code related fields
    cache_key: Optional[str] = Field(
        title="The cache key of the step run.",
        default=None,
        max_length=STR_FIELD_MAX_LENGTH,
    )
    code_hash: Optional[str] = Field(
        title="The code hash of the step run.",
        default=None,
        max_length=STR_FIELD_MAX_LENGTH,
    )
    docstring: Optional[str] = Field(
        title="The docstring of the step function or class.",
        default=None,
        max_length=TEXT_FIELD_MAX_LENGTH,
    )
    source_code: Optional[str] = Field(
        title="The source code of the step function or class.",
        default=None,
        max_length=TEXT_FIELD_MAX_LENGTH,
    )

    # References
    logs: Optional["LogsResponse"] = Field(
        title="Logs associated with this step run.",
        default=None,
    )
    deployment_id: UUID = Field(
        title="The deployment associated with the step run."
    )
    pipeline_run_id: UUID = Field(
        title="The ID of the pipeline run that this step run belongs to.",
    )
    original_step_run_id: Optional[UUID] = Field(
        title="The ID of the original step run if this step was cached.",
        default=None,
    )
    parent_step_ids: List[UUID] = Field(
        title="The IDs of the parent steps of this step run.",
        default_factory=list,
    )
    run_metadata: Dict[str, MetadataType] = Field(
        title="Metadata associated with this step run.",
        default={},
    )

`StepRunResponseResources`

Class for all resource models associated with the step run entity.

Source code in src/zenml/models/v2/core/step_run.py

class StepRunResponseResources(WorkspaceScopedResponseResources):
    """Class for all resource models associated with the step run entity."""

    model_version: Optional[ModelVersionResponse] = None

    # TODO: In Pydantic v2, the `model_` is a protected namespaces for all
    #  fields defined under base models. If not handled, this raises a warning.
    #  It is possible to suppress this warning message with the following
    #  configuration, however the ultimate solution is to rename these fields.
    #  Even though they do not cause any problems right now, if we are not
    #  careful we might overwrite some fields protected by pydantic.
    model_config = ConfigDict(protected_namespaces=())

`StepRunUpdate`

Bases: BaseModel

Update model for step runs.

Source code in src/zenml/models/v2/core/step_run.py

class StepRunUpdate(BaseModel):
    """Update model for step runs."""

    outputs: Dict[str, List[UUID]] = Field(
        title="The IDs of the output artifact versions of the step run.",
        default={},
    )
    loaded_artifact_versions: Dict[str, UUID] = Field(
        title="The IDs of artifact versions that were loaded by this step run.",
        default={},
    )
    status: Optional[ExecutionStatus] = Field(
        title="The status of the step.",
        default=None,
    )
    end_time: Optional[datetime] = Field(
        title="The end time of the step run.",
        default=None,
    )
    model_version_id: Optional[UUID] = Field(
        title="The ID of the model version that was "
        "configured by this step run explicitly.",
        default=None,
    )
    model_config = ConfigDict(protected_namespaces=())

`StrFilter`

Bases: Filter

Filter for all string fields.

Source code in src/zenml/models/v2/base/filter.py

class StrFilter(Filter):
    """Filter for all string fields."""

    json_encode_value: bool = False

    ALLOWED_OPS: ClassVar[List[str]] = [
        GenericFilterOps.EQUALS,
        GenericFilterOps.NOT_EQUALS,
        GenericFilterOps.STARTSWITH,
        GenericFilterOps.CONTAINS,
        GenericFilterOps.ENDSWITH,
        GenericFilterOps.ONEOF,
        GenericFilterOps.GT,
        GenericFilterOps.GTE,
        GenericFilterOps.LT,
        GenericFilterOps.LTE,
    ]

    @model_validator(mode="after")
    def check_value_if_operation_oneof(self) -> "StrFilter":
        """Validator to check if value is a list if oneof operation is used.

        Raises:
            ValueError: If the value is not a list

        Returns:
            self
        """
        if self.operation == GenericFilterOps.ONEOF:
            if not isinstance(self.value, list):
                raise ValueError(ONEOF_ERROR)
        return self

    def generate_query_conditions_from_column(self, column: Any) -> Any:
        """Generate query conditions for a string column.

        Args:
            column: The string column of an SQLModel table on which to filter.

        Returns:
            A list of query conditions.

        Raises:
            ValueError: the comparison of the column to a numeric value fails.
        """
        if self.operation in {
            GenericFilterOps.GT,
            GenericFilterOps.LT,
            GenericFilterOps.GTE,
            GenericFilterOps.LTE,
        }:
            try:
                numeric_column = cast(column, Float)

                assert self.value is not None

                if self.operation == GenericFilterOps.GT:
                    return and_(
                        numeric_column, numeric_column > float(self.value)
                    )
                if self.operation == GenericFilterOps.LT:
                    return and_(
                        numeric_column, numeric_column < float(self.value)
                    )
                if self.operation == GenericFilterOps.GTE:
                    return and_(
                        numeric_column, numeric_column >= float(self.value)
                    )
                if self.operation == GenericFilterOps.LTE:
                    return and_(
                        numeric_column, numeric_column <= float(self.value)
                    )
            except Exception as e:
                raise ValueError(
                    f"Failed to compare the column '{column}' to the "
                    f"value '{self.value}' (must be numeric): {e}"
                )

        if self.operation == GenericFilterOps.ONEOF:
            assert isinstance(self.value, list)
            # Convert the list of values to a list of json strings
            json_list = (
                [json.dumps(v) for v in self.value]
                if self.json_encode_value
                else self.value
            )
            return column.in_(json_list)

        # Don't convert the value to a json string if the operation is contains
        # because the quotes around strings will mess with the comparison
        if self.operation == GenericFilterOps.CONTAINS:
            return column.like(f"%{self.value}%")

        json_value = (
            json.dumps(self.value) if self.json_encode_value else self.value
        )

        if self.operation == GenericFilterOps.STARTSWITH:
            return column.startswith(f"{json_value}")
        if self.operation == GenericFilterOps.ENDSWITH:
            return column.endswith(f"{json_value}")
        if self.operation == GenericFilterOps.NOT_EQUALS:
            return column != json_value

        return column == json_value

`check_value_if_operation_oneof()`

Validator to check if value is a list if oneof operation is used.

Raises:

Type	Description
`ValueError`	If the value is not a list

Returns:

Type	Description
`StrFilter`	self

Source code in src/zenml/models/v2/base/filter.py

@model_validator(mode="after")
def check_value_if_operation_oneof(self) -> "StrFilter":
    """Validator to check if value is a list if oneof operation is used.

    Raises:
        ValueError: If the value is not a list

    Returns:
        self
    """
    if self.operation == GenericFilterOps.ONEOF:
        if not isinstance(self.value, list):
            raise ValueError(ONEOF_ERROR)
    return self

`generate_query_conditions_from_column(column)`

Generate query conditions for a string column.

Parameters:

Name	Type	Description	Default
`column`	`Any`	The string column of an SQLModel table on which to filter.	required

Returns:

Type	Description
`Any`	A list of query conditions.

Raises:

Type	Description
`ValueError`	the comparison of the column to a numeric value fails.

Source code in src/zenml/models/v2/base/filter.py

def generate_query_conditions_from_column(self, column: Any) -> Any:
    """Generate query conditions for a string column.

    Args:
        column: The string column of an SQLModel table on which to filter.

    Returns:
        A list of query conditions.

    Raises:
        ValueError: the comparison of the column to a numeric value fails.
    """
    if self.operation in {
        GenericFilterOps.GT,
        GenericFilterOps.LT,
        GenericFilterOps.GTE,
        GenericFilterOps.LTE,
    }:
        try:
            numeric_column = cast(column, Float)

            assert self.value is not None

            if self.operation == GenericFilterOps.GT:
                return and_(
                    numeric_column, numeric_column > float(self.value)
                )
            if self.operation == GenericFilterOps.LT:
                return and_(
                    numeric_column, numeric_column < float(self.value)
                )
            if self.operation == GenericFilterOps.GTE:
                return and_(
                    numeric_column, numeric_column >= float(self.value)
                )
            if self.operation == GenericFilterOps.LTE:
                return and_(
                    numeric_column, numeric_column <= float(self.value)
                )
        except Exception as e:
            raise ValueError(
                f"Failed to compare the column '{column}' to the "
                f"value '{self.value}' (must be numeric): {e}"
            )

    if self.operation == GenericFilterOps.ONEOF:
        assert isinstance(self.value, list)
        # Convert the list of values to a list of json strings
        json_list = (
            [json.dumps(v) for v in self.value]
            if self.json_encode_value
            else self.value
        )
        return column.in_(json_list)

    # Don't convert the value to a json string if the operation is contains
    # because the quotes around strings will mess with the comparison
    if self.operation == GenericFilterOps.CONTAINS:
        return column.like(f"%{self.value}%")

    json_value = (
        json.dumps(self.value) if self.json_encode_value else self.value
    )

    if self.operation == GenericFilterOps.STARTSWITH:
        return column.startswith(f"{json_value}")
    if self.operation == GenericFilterOps.ENDSWITH:
        return column.endswith(f"{json_value}")
    if self.operation == GenericFilterOps.NOT_EQUALS:
        return column != json_value

    return column == json_value

`TagFilter`

Bases: BaseFilter

Model to enable advanced filtering of all tags.

Source code in src/zenml/models/v2/core/tag.py

class TagFilter(BaseFilter):
    """Model to enable advanced filtering of all tags."""

    name: Optional[str] = Field(
        description="The unique title of the tag.", default=None
    )
    color: Optional[ColorVariants] = Field(
        description="The color variant assigned to the tag.", default=None
    )

`TagRequest`

Bases: BaseRequest

Request model for tags.

Source code in src/zenml/models/v2/core/tag.py

class TagRequest(BaseRequest):
    """Request model for tags."""

    name: str = Field(
        description="The unique title of the tag.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    color: ColorVariants = Field(
        description="The color variant assigned to the tag.",
        default_factory=lambda: random.choice(list(ColorVariants)),
    )

`TagResourceRequest`

Bases: BaseRequest

Request model for links between tags and resources.

Source code in src/zenml/models/v2/core/tag_resource.py

class TagResourceRequest(BaseRequest):
    """Request model for links between tags and resources."""

    tag_id: UUID
    resource_id: UUID
    resource_type: TaggableResourceTypes

`TagResourceResponse`

Bases: BaseIdentifiedResponse[TagResourceResponseBody, BaseResponseMetadata, TagResourceResponseResources]

Response model for the links between tags and resources.

Source code in src/zenml/models/v2/core/tag_resource.py

class TagResourceResponse(
    BaseIdentifiedResponse[
        TagResourceResponseBody,
        BaseResponseMetadata,
        TagResourceResponseResources,
    ]
):
    """Response model for the links between tags and resources."""

    @property
    def tag_id(self) -> UUID:
        """The `tag_id` property.

        Returns:
            the value of the property.
        """
        return self.get_body().tag_id

    @property
    def resource_id(self) -> UUID:
        """The `resource_id` property.

        Returns:
            the value of the property.
        """
        return self.get_body().resource_id

    @property
    def resource_type(self) -> TaggableResourceTypes:
        """The `resource_type` property.

        Returns:
            the value of the property.
        """
        return self.get_body().resource_type

`resource_id` `property`

The resource_id property.

Returns:

Type	Description
`UUID`	the value of the property.

`resource_type` `property`

The resource_type property.

Returns:

Type	Description
`TaggableResourceTypes`	the value of the property.

`tag_id` `property`

The tag_id property.

Returns:

Type	Description
`UUID`	the value of the property.

`TagResourceResponseBody`

Response body for the links between tags and resources.

Source code in src/zenml/models/v2/core/tag_resource.py

class TagResourceResponseBody(BaseDatedResponseBody):
    """Response body for the links between tags and resources."""

    tag_id: UUID
    resource_id: UUID
    resource_type: TaggableResourceTypes

`TagResponse`

Bases: BaseIdentifiedResponse[TagResponseBody, BaseResponseMetadata, TagResponseResources]

Response model for tags.

Source code in src/zenml/models/v2/core/tag.py

class TagResponse(
    BaseIdentifiedResponse[
        TagResponseBody, BaseResponseMetadata, TagResponseResources
    ]
):
    """Response model for tags."""

    name: str = Field(
        description="The unique title of the tag.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    def get_hydrated_version(self) -> "TagResponse":
        """Get the hydrated version of this tag.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_tag(self.id)

    @property
    def color(self) -> ColorVariants:
        """The `color` property.

        Returns:
            the value of the property.
        """
        return self.get_body().color

    @property
    def tagged_count(self) -> int:
        """The `tagged_count` property.

        Returns:
            the value of the property.
        """
        return self.get_body().tagged_count

`color` `property`

The color property.

Returns:

Type	Description
`ColorVariants`	the value of the property.

`tagged_count` `property`

The tagged_count property.

Returns:

Type	Description
`int`	the value of the property.

`get_hydrated_version()`

Get the hydrated version of this tag.

Returns:

Type	Description
`TagResponse`	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/tag.py

def get_hydrated_version(self) -> "TagResponse":
    """Get the hydrated version of this tag.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_tag(self.id)

`TagResponseBody`

Response body for tags.

Source code in src/zenml/models/v2/core/tag.py

class TagResponseBody(BaseDatedResponseBody):
    """Response body for tags."""

    color: ColorVariants = Field(
        description="The color variant assigned to the tag.",
        default_factory=lambda: random.choice(list(ColorVariants)),
    )
    tagged_count: int = Field(
        description="The count of resources tagged with this tag."
    )

`TagUpdate`

Bases: BaseModel

Update model for tags.

Source code in src/zenml/models/v2/core/tag.py

class TagUpdate(BaseModel):
    """Update model for tags."""

    name: Optional[str] = None
    color: Optional[ColorVariants] = None

`TaggableFilter`

Bases: BaseFilter

Model to enable filtering and sorting by tags.

Source code in src/zenml/models/v2/base/scoped.py

class TaggableFilter(BaseFilter):
    """Model to enable filtering and sorting by tags."""

    tag: Optional[str] = Field(
        description="Tag to apply to the filter query.", default=None
    )

    FILTER_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        *BaseFilter.FILTER_EXCLUDE_FIELDS,
        "tag",
    ]
    CUSTOM_SORTING_OPTIONS: ClassVar[List[str]] = [
        *BaseFilter.CUSTOM_SORTING_OPTIONS,
        "tags",
    ]

    def apply_filter(
        self,
        query: AnyQuery,
        table: Type["AnySchema"],
    ) -> AnyQuery:
        """Applies the filter to a query.

        Args:
            query: The query to which to apply the filter.
            table: The query table.

        Returns:
            The query with filter applied.
        """
        from zenml.zen_stores.schemas import TagResourceSchema, TagSchema

        query = super().apply_filter(query=query, table=table)
        if self.tag:
            query = query.join(
                TagResourceSchema,
                TagResourceSchema.resource_id == getattr(table, "id"),
            ).join(TagSchema, TagSchema.id == TagResourceSchema.tag_id)

        return query

    def get_custom_filters(
        self, table: Type["AnySchema"]
    ) -> List["ColumnElement[bool]"]:
        """Get custom tag filters.

        Args:
            table: The query table.

        Returns:
            A list of custom filters.
        """
        from zenml.zen_stores.schemas import TagSchema

        custom_filters = super().get_custom_filters(table)
        if self.tag:
            custom_filters.append(
                self.generate_custom_query_conditions_for_column(
                    value=self.tag, table=TagSchema, column="name"
                )
            )

        return custom_filters

    def apply_sorting(
        self,
        query: AnyQuery,
        table: Type["AnySchema"],
    ) -> AnyQuery:
        """Apply sorting to the query.

        Args:
            query: The query to which to apply the sorting.
            table: The query table.

        Returns:
            The query with sorting applied.
        """
        sort_by, operand = self.sorting_params

        if sort_by == "tags":
            from sqlmodel import asc, desc, func, select

            from zenml.enums import SorterOps, TaggableResourceTypes
            from zenml.zen_stores.schemas import (
                ArtifactSchema,
                ArtifactVersionSchema,
                ModelSchema,
                ModelVersionSchema,
                PipelineRunSchema,
                PipelineSchema,
                RunTemplateSchema,
                TagResourceSchema,
                TagSchema,
            )

            resource_type_mapping = {
                ArtifactSchema: TaggableResourceTypes.ARTIFACT,
                ArtifactVersionSchema: TaggableResourceTypes.ARTIFACT_VERSION,
                ModelSchema: TaggableResourceTypes.MODEL,
                ModelVersionSchema: TaggableResourceTypes.MODEL_VERSION,
                PipelineSchema: TaggableResourceTypes.PIPELINE,
                PipelineRunSchema: TaggableResourceTypes.PIPELINE_RUN,
                RunTemplateSchema: TaggableResourceTypes.RUN_TEMPLATE,
            }

            sorted_tags = (
                select(TagResourceSchema.resource_id, TagSchema.name)
                .join(TagSchema, TagResourceSchema.tag_id == TagSchema.id)  # type: ignore[arg-type]
                .filter(
                    TagResourceSchema.resource_type  # type: ignore[arg-type]
                    == resource_type_mapping[table]
                )
                .order_by(
                    asc(TagResourceSchema.resource_id), asc(TagSchema.name)
                )
            ).alias("sorted_tags")

            tags_subquery = (
                select(
                    sorted_tags.c.resource_id,
                    func.group_concat(sorted_tags.c.name, ", ").label(
                        "tags_list"
                    ),
                ).group_by(sorted_tags.c.resource_id)
            ).alias("tags_subquery")

            query = query.add_columns(tags_subquery.c.tags_list).outerjoin(
                tags_subquery, table.id == tags_subquery.c.resource_id
            )

            # Apply ordering based on the tags list
            if operand == SorterOps.ASCENDING:
                query = query.order_by(asc("tags_list"))
            else:
                query = query.order_by(desc("tags_list"))

            return query

        return super().apply_sorting(query=query, table=table)

`apply_filter(query, table)`

Applies the filter to a query.

Parameters:

Name	Type	Description	Default
`query`	`AnyQuery`	The query to which to apply the filter.	required
`table`	`Type[AnySchema]`	The query table.	required

Returns:

Type	Description
`AnyQuery`	The query with filter applied.

Source code in src/zenml/models/v2/base/scoped.py

def apply_filter(
    self,
    query: AnyQuery,
    table: Type["AnySchema"],
) -> AnyQuery:
    """Applies the filter to a query.

    Args:
        query: The query to which to apply the filter.
        table: The query table.

    Returns:
        The query with filter applied.
    """
    from zenml.zen_stores.schemas import TagResourceSchema, TagSchema

    query = super().apply_filter(query=query, table=table)
    if self.tag:
        query = query.join(
            TagResourceSchema,
            TagResourceSchema.resource_id == getattr(table, "id"),
        ).join(TagSchema, TagSchema.id == TagResourceSchema.tag_id)

    return query

`apply_sorting(query, table)`

Apply sorting to the query.

Parameters:

Name	Type	Description	Default
`query`	`AnyQuery`	The query to which to apply the sorting.	required
`table`	`Type[AnySchema]`	The query table.	required

Returns:

Type	Description
`AnyQuery`	The query with sorting applied.

Source code in src/zenml/models/v2/base/scoped.py

def apply_sorting(
    self,
    query: AnyQuery,
    table: Type["AnySchema"],
) -> AnyQuery:
    """Apply sorting to the query.

    Args:
        query: The query to which to apply the sorting.
        table: The query table.

    Returns:
        The query with sorting applied.
    """
    sort_by, operand = self.sorting_params

    if sort_by == "tags":
        from sqlmodel import asc, desc, func, select

        from zenml.enums import SorterOps, TaggableResourceTypes
        from zenml.zen_stores.schemas import (
            ArtifactSchema,
            ArtifactVersionSchema,
            ModelSchema,
            ModelVersionSchema,
            PipelineRunSchema,
            PipelineSchema,
            RunTemplateSchema,
            TagResourceSchema,
            TagSchema,
        )

        resource_type_mapping = {
            ArtifactSchema: TaggableResourceTypes.ARTIFACT,
            ArtifactVersionSchema: TaggableResourceTypes.ARTIFACT_VERSION,
            ModelSchema: TaggableResourceTypes.MODEL,
            ModelVersionSchema: TaggableResourceTypes.MODEL_VERSION,
            PipelineSchema: TaggableResourceTypes.PIPELINE,
            PipelineRunSchema: TaggableResourceTypes.PIPELINE_RUN,
            RunTemplateSchema: TaggableResourceTypes.RUN_TEMPLATE,
        }

        sorted_tags = (
            select(TagResourceSchema.resource_id, TagSchema.name)
            .join(TagSchema, TagResourceSchema.tag_id == TagSchema.id)  # type: ignore[arg-type]
            .filter(
                TagResourceSchema.resource_type  # type: ignore[arg-type]
                == resource_type_mapping[table]
            )
            .order_by(
                asc(TagResourceSchema.resource_id), asc(TagSchema.name)
            )
        ).alias("sorted_tags")

        tags_subquery = (
            select(
                sorted_tags.c.resource_id,
                func.group_concat(sorted_tags.c.name, ", ").label(
                    "tags_list"
                ),
            ).group_by(sorted_tags.c.resource_id)
        ).alias("tags_subquery")

        query = query.add_columns(tags_subquery.c.tags_list).outerjoin(
            tags_subquery, table.id == tags_subquery.c.resource_id
        )

        # Apply ordering based on the tags list
        if operand == SorterOps.ASCENDING:
            query = query.order_by(asc("tags_list"))
        else:
            query = query.order_by(desc("tags_list"))

        return query

    return super().apply_sorting(query=query, table=table)

`get_custom_filters(table)`

Get custom tag filters.

Parameters:

Name	Type	Description	Default
`table`	`Type[AnySchema]`	The query table.	required

Returns:

Type	Description
`List[ColumnElement[bool]]`	A list of custom filters.

Source code in src/zenml/models/v2/base/scoped.py

def get_custom_filters(
    self, table: Type["AnySchema"]
) -> List["ColumnElement[bool]"]:
    """Get custom tag filters.

    Args:
        table: The query table.

    Returns:
        A list of custom filters.
    """
    from zenml.zen_stores.schemas import TagSchema

    custom_filters = super().get_custom_filters(table)
    if self.tag:
        custom_filters.append(
            self.generate_custom_query_conditions_for_column(
                value=self.tag, table=TagSchema, column="name"
            )
        )

    return custom_filters

`TriggerExecutionFilter`

Model to enable advanced filtering of all trigger executions.

Source code in src/zenml/models/v2/core/trigger_execution.py

class TriggerExecutionFilter(WorkspaceScopedFilter):
    """Model to enable advanced filtering of all trigger executions."""

    trigger_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="ID of the trigger of the execution.",
        union_mode="left_to_right",
    )

`TriggerExecutionRequest`

Bases: BaseRequest

Model for creating a new Trigger execution.

Source code in src/zenml/models/v2/core/trigger_execution.py

class TriggerExecutionRequest(BaseRequest):
    """Model for creating a new Trigger execution."""

    trigger: UUID
    event_metadata: Dict[str, Any] = {}

`TriggerExecutionResponse`

Bases: BaseIdentifiedResponse[TriggerExecutionResponseBody, TriggerExecutionResponseMetadata, TriggerExecutionResponseResources]

Response model for trigger executions.

Source code in src/zenml/models/v2/core/trigger_execution.py

class TriggerExecutionResponse(
    BaseIdentifiedResponse[
        TriggerExecutionResponseBody,
        TriggerExecutionResponseMetadata,
        TriggerExecutionResponseResources,
    ]
):
    """Response model for trigger executions."""

    def get_hydrated_version(self) -> "TriggerExecutionResponse":
        """Get the hydrated version of this trigger execution.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_trigger_execution(self.id)

    # Body and metadata properties

    @property
    def trigger(self) -> "TriggerResponse":
        """The `trigger` property.

        Returns:
            the value of the property.
        """
        return self.get_resources().trigger

    @property
    def event_metadata(self) -> Dict[str, Any]:
        """The `event_metadata` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().event_metadata

`event_metadata` `property`

The event_metadata property.

Returns:

Type	Description
`Dict[str, Any]`	the value of the property.

`trigger` `property`

The trigger property.

Returns:

Type	Description
`TriggerResponse`	the value of the property.

`get_hydrated_version()`

Get the hydrated version of this trigger execution.

Returns:

Type	Description
`TriggerExecutionResponse`	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/trigger_execution.py

def get_hydrated_version(self) -> "TriggerExecutionResponse":
    """Get the hydrated version of this trigger execution.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_trigger_execution(self.id)

`TriggerExecutionResponseBody`

Response body for trigger executions.

Source code in src/zenml/models/v2/core/trigger_execution.py

class TriggerExecutionResponseBody(BaseDatedResponseBody):
    """Response body for trigger executions."""

`TriggerExecutionResponseMetadata`

Bases: BaseResponseMetadata

Response metadata for trigger executions.

Source code in src/zenml/models/v2/core/trigger_execution.py

class TriggerExecutionResponseMetadata(BaseResponseMetadata):
    """Response metadata for trigger executions."""

    event_metadata: Dict[str, Any] = {}

`TriggerExecutionResponseResources`

Bases: BaseResponseResources

Class for all resource models associated with the trigger entity.

Source code in src/zenml/models/v2/core/trigger_execution.py

class TriggerExecutionResponseResources(BaseResponseResources):
    """Class for all resource models associated with the trigger entity."""

    trigger: "TriggerResponse" = Field(
        title="The event source that activates this trigger.",
    )

`TriggerFilter`

Model to enable advanced filtering of all triggers.

Source code in src/zenml/models/v2/core/trigger.py

class TriggerFilter(WorkspaceScopedFilter):
    """Model to enable advanced filtering of all triggers."""

    FILTER_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        *WorkspaceScopedFilter.FILTER_EXCLUDE_FIELDS,
        "action_flavor",
        "action_subtype",
        "event_source_flavor",
        "event_source_subtype",
    ]

    name: Optional[str] = Field(
        default=None,
        description="Name of the trigger.",
    )
    event_source_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="The event source this trigger is attached to.",
        union_mode="left_to_right",
    )
    action_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="The action this trigger is attached to.",
        union_mode="left_to_right",
    )
    is_active: Optional[bool] = Field(
        default=None,
        description="Whether the trigger is active.",
    )
    action_flavor: Optional[str] = Field(
        default=None,
        title="The flavor of the action that is executed by this trigger.",
    )
    action_subtype: Optional[str] = Field(
        default=None,
        title="The subtype of the action that is executed by this trigger.",
    )
    event_source_flavor: Optional[str] = Field(
        default=None,
        title="The flavor of the event source that activates this trigger.",
    )
    event_source_subtype: Optional[str] = Field(
        default=None,
        title="The subtype of the event source that activates this trigger.",
    )

    def get_custom_filters(
        self, table: Type["AnySchema"]
    ) -> List["ColumnElement[bool]"]:
        """Get custom filters.

        Args:
            table: The query table.

        Returns:
            A list of custom filters.
        """
        from sqlmodel import and_

        from zenml.zen_stores.schemas import (
            ActionSchema,
            EventSourceSchema,
            TriggerSchema,
        )

        custom_filters = super().get_custom_filters(table)

        if self.event_source_flavor:
            event_source_flavor_filter = and_(
                EventSourceSchema.id == TriggerSchema.event_source_id,
                EventSourceSchema.flavor == self.event_source_flavor,
            )
            custom_filters.append(event_source_flavor_filter)

        if self.event_source_subtype:
            event_source_subtype_filter = and_(
                EventSourceSchema.id == TriggerSchema.event_source_id,
                EventSourceSchema.plugin_subtype == self.event_source_subtype,
            )
            custom_filters.append(event_source_subtype_filter)

        if self.action_flavor:
            action_flavor_filter = and_(
                ActionSchema.id == TriggerSchema.action_id,
                ActionSchema.flavor == self.action_flavor,
            )
            custom_filters.append(action_flavor_filter)

        if self.action_subtype:
            action_subtype_filter = and_(
                ActionSchema.id == TriggerSchema.action_id,
                ActionSchema.plugin_subtype == self.action_subtype,
            )
            custom_filters.append(action_subtype_filter)

        return custom_filters

`get_custom_filters(table)`

Get custom filters.

Parameters:

Name	Type	Description	Default
`table`	`Type[AnySchema]`	The query table.	required

Returns:

Type	Description
`List[ColumnElement[bool]]`	A list of custom filters.

Source code in src/zenml/models/v2/core/trigger.py

def get_custom_filters(
    self, table: Type["AnySchema"]
) -> List["ColumnElement[bool]"]:
    """Get custom filters.

    Args:
        table: The query table.

    Returns:
        A list of custom filters.
    """
    from sqlmodel import and_

    from zenml.zen_stores.schemas import (
        ActionSchema,
        EventSourceSchema,
        TriggerSchema,
    )

    custom_filters = super().get_custom_filters(table)

    if self.event_source_flavor:
        event_source_flavor_filter = and_(
            EventSourceSchema.id == TriggerSchema.event_source_id,
            EventSourceSchema.flavor == self.event_source_flavor,
        )
        custom_filters.append(event_source_flavor_filter)

    if self.event_source_subtype:
        event_source_subtype_filter = and_(
            EventSourceSchema.id == TriggerSchema.event_source_id,
            EventSourceSchema.plugin_subtype == self.event_source_subtype,
        )
        custom_filters.append(event_source_subtype_filter)

    if self.action_flavor:
        action_flavor_filter = and_(
            ActionSchema.id == TriggerSchema.action_id,
            ActionSchema.flavor == self.action_flavor,
        )
        custom_filters.append(action_flavor_filter)

    if self.action_subtype:
        action_subtype_filter = and_(
            ActionSchema.id == TriggerSchema.action_id,
            ActionSchema.plugin_subtype == self.action_subtype,
        )
        custom_filters.append(action_subtype_filter)

    return custom_filters

`TriggerRequest`

Model for creating a new trigger.

Source code in src/zenml/models/v2/core/trigger.py

class TriggerRequest(WorkspaceScopedRequest):
    """Model for creating a new trigger."""

    name: str = Field(
        title="The name of the trigger.", max_length=STR_FIELD_MAX_LENGTH
    )
    description: str = Field(
        default="",
        title="The description of the trigger",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    action_id: UUID = Field(
        title="The action that is executed by this trigger.",
    )
    schedule: Optional[Schedule] = Field(
        default=None,
        title="The schedule for the trigger. Either a schedule or an event "
        "source is required.",
    )
    event_source_id: Optional[UUID] = Field(
        default=None,
        title="The event source that activates this trigger. Either a schedule "
        "or an event source is required.",
    )
    event_filter: Optional[Dict[str, Any]] = Field(
        default=None,
        title="Filter applied to events that activate this trigger. Only "
        "set if the trigger is activated by an event source.",
    )

    @model_validator(mode="after")
    def _validate_schedule_or_event_source(self) -> "TriggerRequest":
        """Validate that either a schedule or an event source is provided.

        Returns:
            The validated request.

        Raises:
            ValueError: If neither a schedule nor an event source is provided,
                or if both are provided.
        """
        if not self.schedule and not self.event_source_id:
            raise ValueError(
                "Either a schedule or an event source is required."
            )

        if self.schedule and self.event_source_id:
            raise ValueError("Only a schedule or an event source is allowed.")

        return self

`TriggerResponse`

Bases: WorkspaceScopedResponse[TriggerResponseBody, TriggerResponseMetadata, TriggerResponseResources]

Response model for models.

Source code in src/zenml/models/v2/core/trigger.py

class TriggerResponse(
    WorkspaceScopedResponse[
        TriggerResponseBody, TriggerResponseMetadata, TriggerResponseResources
    ]
):
    """Response model for models."""

    name: str = Field(
        title="The name of the trigger",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    def get_hydrated_version(self) -> "TriggerResponse":
        """Get the hydrated version of this trigger.

        Returns:
            An instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_trigger(self.id)

    @property
    def action_flavor(self) -> str:
        """The `action_flavor` property.

        Returns:
            the value of the property.
        """
        return self.get_body().action_flavor

    @property
    def action_subtype(self) -> str:
        """The `action_subtype` property.

        Returns:
            the value of the property.
        """
        return self.get_body().action_subtype

    @property
    def event_source_flavor(self) -> Optional[str]:
        """The `event_source_flavor` property.

        Returns:
            the value of the property.
        """
        return self.get_body().event_source_flavor

    @property
    def event_source_subtype(self) -> Optional[str]:
        """The `event_source_subtype` property.

        Returns:
            the value of the property.
        """
        return self.get_body().event_source_subtype

    @property
    def is_active(self) -> bool:
        """The `is_active` property.

        Returns:
            the value of the property.
        """
        return self.get_body().is_active

    @property
    def event_filter(self) -> Optional[Dict[str, Any]]:
        """The `event_filter` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().event_filter

    @property
    def description(self) -> str:
        """The `description` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().description

    @property
    def action(self) -> "ActionResponse":
        """The `action` property.

        Returns:
            the value of the property.
        """
        return self.get_resources().action

    @property
    def event_source(self) -> Optional["EventSourceResponse"]:
        """The `event_source` property.

        Returns:
            the value of the property.
        """
        return self.get_resources().event_source

    @property
    def executions(self) -> Page[TriggerExecutionResponse]:
        """The `event_source` property.

        Returns:
            the value of the property.
        """
        return self.get_resources().executions

`action` `property`

The action property.

Returns:

Type	Description
`ActionResponse`	the value of the property.

`action_flavor` `property`

The action_flavor property.

Returns:

Type	Description
`str`	the value of the property.

`action_subtype` `property`

The action_subtype property.

Returns:

Type	Description
`str`	the value of the property.

`description` `property`

The description property.

Returns:

Type	Description
`str`	the value of the property.

`event_filter` `property`

The event_filter property.

Returns:

Type	Description
`Optional[Dict[str, Any]]`	the value of the property.

`event_source` `property`

The event_source property.

Returns:

Type	Description
`Optional[EventSourceResponse]`	the value of the property.

`event_source_flavor` `property`

The event_source_flavor property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`event_source_subtype` `property`

The event_source_subtype property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`executions` `property`

The event_source property.

Returns:

Type	Description
`Page[TriggerExecutionResponse]`	the value of the property.

`is_active` `property`

The is_active property.

Returns:

Type	Description
`bool`	the value of the property.

`get_hydrated_version()`

Get the hydrated version of this trigger.

Returns:

Type	Description
`TriggerResponse`	An instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/trigger.py

def get_hydrated_version(self) -> "TriggerResponse":
    """Get the hydrated version of this trigger.

    Returns:
        An instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_trigger(self.id)

`TriggerResponseBody`

Response body for triggers.

Source code in src/zenml/models/v2/core/trigger.py

class TriggerResponseBody(WorkspaceScopedResponseBody):
    """Response body for triggers."""

    action_flavor: str = Field(
        title="The flavor of the action that is executed by this trigger.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    action_subtype: str = Field(
        title="The subtype of the action that is executed by this trigger.",
    )
    event_source_flavor: Optional[str] = Field(
        default=None,
        title="The flavor of the event source that activates this trigger. Not "
        "set if the trigger is activated by a schedule.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    event_source_subtype: Optional[str] = Field(
        default=None,
        title="The subtype of the event source that activates this trigger. "
        "Not set if the trigger is activated by a schedule.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    is_active: bool = Field(
        title="Whether the trigger is active.",
    )

`TriggerResponseMetadata`

Response metadata for triggers.

Source code in src/zenml/models/v2/core/trigger.py

class TriggerResponseMetadata(WorkspaceScopedResponseMetadata):
    """Response metadata for triggers."""

    description: str = Field(
        default="",
        title="The description of the trigger.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    event_filter: Optional[Dict[str, Any]] = Field(
        default=None,
        title="The event that activates this trigger. Not set if the trigger "
        "is activated by a schedule.",
    )
    schedule: Optional[Schedule] = Field(
        default=None,
        title="The schedule that activates this trigger. Not set if the "
        "trigger is activated by an event source.",
    )

`TriggerResponseResources`

Class for all resource models associated with the trigger entity.

Source code in src/zenml/models/v2/core/trigger.py

class TriggerResponseResources(WorkspaceScopedResponseResources):
    """Class for all resource models associated with the trigger entity."""

    action: "ActionResponse" = Field(
        title="The action that is executed by this trigger.",
    )
    event_source: Optional["EventSourceResponse"] = Field(
        default=None,
        title="The event source that activates this trigger. Not set if the "
        "trigger is activated by a schedule.",
    )
    executions: Page[TriggerExecutionResponse] = Field(
        title="The executions of this trigger.",
    )

`TriggerUpdate`

Bases: BaseUpdate

Update model for triggers.

Source code in src/zenml/models/v2/core/trigger.py

class TriggerUpdate(BaseUpdate):
    """Update model for triggers."""

    name: Optional[str] = Field(
        default=None,
        title="The new name for the trigger.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    description: Optional[str] = Field(
        default=None,
        title="The new description for the trigger.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    event_filter: Optional[Dict[str, Any]] = Field(
        default=None,
        title="New filter applied to events that activate this trigger. Only "
        "valid if the trigger is already configured to be activated by an "
        "event source.",
    )
    schedule: Optional[Schedule] = Field(
        default=None,
        title="The updated schedule for the trigger. Only valid if the trigger "
        "is already configured to be activated by a schedule.",
    )
    is_active: Optional[bool] = Field(
        default=None,
        title="The new status of the trigger.",
    )

`UUIDFilter`

Bases: StrFilter

Filter for all uuid fields which are mostly treated like strings.

Source code in src/zenml/models/v2/base/filter.py

class UUIDFilter(StrFilter):
    """Filter for all uuid fields which are mostly treated like strings."""

    @field_validator("value", mode="before")
    @classmethod
    def _remove_hyphens_from_value(cls, value: Any) -> Any:
        """Remove hyphens from the value to enable string comparisons.

        Args:
            value: The filter value.

        Returns:
            The filter value with removed hyphens.
        """
        if isinstance(value, str):
            return value.replace("-", "")

        if isinstance(value, list):
            return [str(v).replace("-", "") for v in value]

        return value

    def generate_query_conditions_from_column(self, column: Any) -> Any:
        """Generate query conditions for a UUID column.

        Args:
            column: The UUID column of an SQLModel table on which to filter.

        Returns:
            A list of query conditions.
        """
        import sqlalchemy
        from sqlalchemy_utils.functions import cast_if

        from zenml.utils import uuid_utils

        # For equality checks, compare the UUID directly
        if self.operation == GenericFilterOps.EQUALS:
            if not uuid_utils.is_valid_uuid(self.value):
                return False

            return column == self.value

        if self.operation == GenericFilterOps.NOT_EQUALS:
            if not uuid_utils.is_valid_uuid(self.value):
                return True

            return column != self.value

        # For all other operations, cast and handle the column as string
        return super().generate_query_conditions_from_column(
            column=cast_if(column, sqlalchemy.String)
        )

`generate_query_conditions_from_column(column)`

Generate query conditions for a UUID column.

Parameters:

Name	Type	Description	Default
`column`	`Any`	The UUID column of an SQLModel table on which to filter.	required

Returns:

Type	Description
`Any`	A list of query conditions.

Source code in src/zenml/models/v2/base/filter.py

def generate_query_conditions_from_column(self, column: Any) -> Any:
    """Generate query conditions for a UUID column.

    Args:
        column: The UUID column of an SQLModel table on which to filter.

    Returns:
        A list of query conditions.
    """
    import sqlalchemy
    from sqlalchemy_utils.functions import cast_if

    from zenml.utils import uuid_utils

    # For equality checks, compare the UUID directly
    if self.operation == GenericFilterOps.EQUALS:
        if not uuid_utils.is_valid_uuid(self.value):
            return False

        return column == self.value

    if self.operation == GenericFilterOps.NOT_EQUALS:
        if not uuid_utils.is_valid_uuid(self.value):
            return True

        return column != self.value

    # For all other operations, cast and handle the column as string
    return super().generate_query_conditions_from_column(
        column=cast_if(column, sqlalchemy.String)
    )

`UserAuthModel`

Bases: BaseZenModel

Authentication Model for the User.

This model is only used server-side. The server endpoints can use this model to authenticate the user credentials (Token, Password).

Source code in src/zenml/models/v2/misc/user_auth.py

class UserAuthModel(BaseZenModel):
    """Authentication Model for the User.

    This model is only used server-side. The server endpoints can use this model
    to authenticate the user credentials (Token, Password).
    """

    id: UUID = Field(title="The unique resource id.")

    created: datetime = Field(title="Time when this resource was created.")
    updated: datetime = Field(
        title="Time when this resource was last updated."
    )

    active: bool = Field(default=False, title="Active account.")
    is_service_account: bool = Field(
        title="Indicates whether this is a service account or a regular user "
        "account."
    )

    activation_token: Optional[PlainSerializedSecretStr] = Field(
        default=None, exclude=True
    )
    password: Optional[PlainSerializedSecretStr] = Field(
        default=None, exclude=True
    )
    name: str = Field(
        title="The unique username for the account.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    full_name: str = Field(
        default="",
        title="The full name for the account owner. Only relevant for user "
        "accounts.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    email_opted_in: Optional[bool] = Field(
        default=None,
        title="Whether the user agreed to share their email. Only relevant for "
        "user accounts",
        description="`null` if not answered, `true` if agreed, "
        "`false` if skipped.",
    )

    @classmethod
    def _get_crypt_context(cls) -> "CryptContext":
        """Returns the password encryption context.

        Returns:
            The password encryption context.
        """
        from passlib.context import CryptContext

        return CryptContext(schemes=["bcrypt"], deprecated="auto")

    @classmethod
    def _is_hashed_secret(cls, secret: SecretStr) -> bool:
        """Checks if a secret value is already hashed.

        Args:
            secret: The secret value to check.

        Returns:
            True if the secret value is hashed, otherwise False.
        """
        return (
            re.match(r"^\$2[ayb]\$.{56}$", secret.get_secret_value())
            is not None
        )

    @classmethod
    def _get_hashed_secret(cls, secret: Optional[SecretStr]) -> Optional[str]:
        """Hashes the input secret and returns the hash value.

        Only applied if supplied and if not already hashed.

        Args:
            secret: The secret value to hash.

        Returns:
            The secret hash value, or None if no secret was supplied.
        """
        if secret is None:
            return None
        if cls._is_hashed_secret(secret):
            return secret.get_secret_value()
        pwd_context = cls._get_crypt_context()
        return pwd_context.hash(secret.get_secret_value())

    def get_password(self) -> Optional[str]:
        """Get the password.

        Returns:
            The password as a plain string, if it exists.
        """
        if self.password is None:
            return None
        return self.password.get_secret_value()

    def get_hashed_password(self) -> Optional[str]:
        """Returns the hashed password, if configured.

        Returns:
            The hashed password.
        """
        return self._get_hashed_secret(self.password)

    def get_hashed_activation_token(self) -> Optional[str]:
        """Returns the hashed activation token, if configured.

        Returns:
            The hashed activation token.
        """
        return self._get_hashed_secret(self.activation_token)

    @classmethod
    def verify_password(
        cls, plain_password: str, user: Optional["UserAuthModel"] = None
    ) -> bool:
        """Verifies a given plain password against the stored password.

        Args:
            plain_password: Input password to be verified.
            user: User for which the password is to be verified.

        Returns:
            True if the passwords match.
        """
        # even when the user or password is not set, we still want to execute
        # the password hash verification to protect against response discrepancy
        # attacks (https://cwe.mitre.org/data/definitions/204.html)
        password_hash: Optional[str] = None
        if (
            user is not None
            # Disable password verification for service accounts as an extra
            # security measure. Service accounts should only be used with API
            # keys.
            and not user.is_service_account
            and user.password is not None
        ):  # and user.active:
            password_hash = user.get_hashed_password()
        pwd_context = cls._get_crypt_context()
        return pwd_context.verify(plain_password, password_hash)

    @classmethod
    def verify_activation_token(
        cls, activation_token: str, user: Optional["UserAuthModel"] = None
    ) -> bool:
        """Verifies a given activation token against the stored token.

        Args:
            activation_token: Input activation token to be verified.
            user: User for which the activation token is to be verified.

        Returns:
            True if the token is valid.
        """
        # even when the user or token is not set, we still want to execute the
        # token hash verification to protect against response discrepancy
        # attacks (https://cwe.mitre.org/data/definitions/204.html)
        token_hash: str = ""
        if (
            user is not None
            # Disable activation tokens for service accounts as an extra
            # security measure. Service accounts should only be used with API
            # keys.
            and not user.is_service_account
            and user.activation_token is not None
            and not user.active
        ):
            token_hash = user.get_hashed_activation_token() or ""
        pwd_context = cls._get_crypt_context()
        return pwd_context.verify(activation_token, token_hash)

`get_hashed_activation_token()`

Returns the hashed activation token, if configured.

Returns:

Type	Description
`Optional[str]`	The hashed activation token.

Source code in src/zenml/models/v2/misc/user_auth.py

def get_hashed_activation_token(self) -> Optional[str]:
    """Returns the hashed activation token, if configured.

    Returns:
        The hashed activation token.
    """
    return self._get_hashed_secret(self.activation_token)

`get_hashed_password()`

Returns the hashed password, if configured.

Returns:

Type	Description
`Optional[str]`	The hashed password.

Source code in src/zenml/models/v2/misc/user_auth.py

def get_hashed_password(self) -> Optional[str]:
    """Returns the hashed password, if configured.

    Returns:
        The hashed password.
    """
    return self._get_hashed_secret(self.password)

`get_password()`

Get the password.

Returns:

Type	Description
`Optional[str]`	The password as a plain string, if it exists.

Source code in src/zenml/models/v2/misc/user_auth.py

def get_password(self) -> Optional[str]:
    """Get the password.

    Returns:
        The password as a plain string, if it exists.
    """
    if self.password is None:
        return None
    return self.password.get_secret_value()

`verify_activation_token(activation_token, user=None)` `classmethod`

Verifies a given activation token against the stored token.

Parameters:

Name	Type	Description	Default
`activation_token`	`str`	Input activation token to be verified.	required
`user`	`Optional[UserAuthModel]`	User for which the activation token is to be verified.	`None`

Returns:

Type	Description
`bool`	True if the token is valid.

Source code in src/zenml/models/v2/misc/user_auth.py

@classmethod
def verify_activation_token(
    cls, activation_token: str, user: Optional["UserAuthModel"] = None
) -> bool:
    """Verifies a given activation token against the stored token.

    Args:
        activation_token: Input activation token to be verified.
        user: User for which the activation token is to be verified.

    Returns:
        True if the token is valid.
    """
    # even when the user or token is not set, we still want to execute the
    # token hash verification to protect against response discrepancy
    # attacks (https://cwe.mitre.org/data/definitions/204.html)
    token_hash: str = ""
    if (
        user is not None
        # Disable activation tokens for service accounts as an extra
        # security measure. Service accounts should only be used with API
        # keys.
        and not user.is_service_account
        and user.activation_token is not None
        and not user.active
    ):
        token_hash = user.get_hashed_activation_token() or ""
    pwd_context = cls._get_crypt_context()
    return pwd_context.verify(activation_token, token_hash)

`verify_password(plain_password, user=None)` `classmethod`

Verifies a given plain password against the stored password.

Parameters:

Name	Type	Description	Default
`plain_password`	`str`	Input password to be verified.	required
`user`	`Optional[UserAuthModel]`	User for which the password is to be verified.	`None`

Returns:

Type	Description
`bool`	True if the passwords match.

Source code in src/zenml/models/v2/misc/user_auth.py

@classmethod
def verify_password(
    cls, plain_password: str, user: Optional["UserAuthModel"] = None
) -> bool:
    """Verifies a given plain password against the stored password.

    Args:
        plain_password: Input password to be verified.
        user: User for which the password is to be verified.

    Returns:
        True if the passwords match.
    """
    # even when the user or password is not set, we still want to execute
    # the password hash verification to protect against response discrepancy
    # attacks (https://cwe.mitre.org/data/definitions/204.html)
    password_hash: Optional[str] = None
    if (
        user is not None
        # Disable password verification for service accounts as an extra
        # security measure. Service accounts should only be used with API
        # keys.
        and not user.is_service_account
        and user.password is not None
    ):  # and user.active:
        password_hash = user.get_hashed_password()
    pwd_context = cls._get_crypt_context()
    return pwd_context.verify(plain_password, password_hash)

`UserFilter`

Bases: BaseFilter

Model to enable advanced filtering of all Users.

Source code in src/zenml/models/v2/core/user.py

class UserFilter(BaseFilter):
    """Model to enable advanced filtering of all Users."""

    name: Optional[str] = Field(
        default=None,
        description="Name of the user",
    )
    full_name: Optional[str] = Field(
        default=None,
        description="Full Name of the user",
    )
    email: Optional[str] = Field(
        default=None,
        description="Email of the user",
    )
    active: Optional[Union[bool, str]] = Field(
        default=None,
        description="Whether the user is active",
        union_mode="left_to_right",
    )
    email_opted_in: Optional[Union[bool, str]] = Field(
        default=None,
        description="Whether the user has opted in to emails",
        union_mode="left_to_right",
    )
    external_user_id: Optional[Union[UUID, str]] = Field(
        default=None,
        title="The external user ID associated with the account.",
        union_mode="left_to_right",
    )

    def apply_filter(
        self,
        query: AnyQuery,
        table: Type["AnySchema"],
    ) -> AnyQuery:
        """Override to filter out service accounts from the query.

        Args:
            query: The query to which to apply the filter.
            table: The query table.

        Returns:
            The query with filter applied.
        """
        query = super().apply_filter(query=query, table=table)
        query = query.where(
            getattr(table, "is_service_account") != True  # noqa: E712
        )

        return query

`apply_filter(query, table)`

Override to filter out service accounts from the query.

Parameters:

Name	Type	Description	Default
`query`	`AnyQuery`	The query to which to apply the filter.	required
`table`	`Type[AnySchema]`	The query table.	required

Returns:

Type	Description
`AnyQuery`	The query with filter applied.

Source code in src/zenml/models/v2/core/user.py

def apply_filter(
    self,
    query: AnyQuery,
    table: Type["AnySchema"],
) -> AnyQuery:
    """Override to filter out service accounts from the query.

    Args:
        query: The query to which to apply the filter.
        table: The query table.

    Returns:
        The query with filter applied.
    """
    query = super().apply_filter(query=query, table=table)
    query = query.where(
        getattr(table, "is_service_account") != True  # noqa: E712
    )

    return query

`UserRequest`

Bases: UserBase, BaseRequest

Request model for users.

Source code in src/zenml/models/v2/core/user.py

class UserRequest(UserBase, BaseRequest):
    """Request model for users."""

    # Analytics fields for user request models
    ANALYTICS_FIELDS: ClassVar[List[str]] = [
        "name",
        "full_name",
        "active",
        "email_opted_in",
    ]

    name: str = Field(
        title="The unique username for the account.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    full_name: str = Field(
        default="",
        title="The full name for the account owner. Only relevant for user "
        "accounts.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    is_admin: bool = Field(
        title="Whether the account is an administrator.",
    )
    active: bool = Field(default=False, title="Whether the account is active.")

    model_config = ConfigDict(
        # Validate attributes when assigning them
        validate_assignment=True,
        # Forbid extra attributes to prevent unexpected behavior
        extra="forbid",
    )

`UserResponse`

Bases: BaseIdentifiedResponse[UserResponseBody, UserResponseMetadata, UserResponseResources]

Response model for user and service accounts.

This returns the activation_token that is required for the user-invitation-flow of the frontend. The email is returned optionally as well for use by the analytics on the client-side.

Source code in src/zenml/models/v2/core/user.py

class UserResponse(
    BaseIdentifiedResponse[
        UserResponseBody, UserResponseMetadata, UserResponseResources
    ]
):
    """Response model for user and service accounts.

    This returns the activation_token that is required for the
    user-invitation-flow of the frontend. The email is returned optionally as
    well for use by the analytics on the client-side.
    """

    ANALYTICS_FIELDS: ClassVar[List[str]] = [
        "name",
        "full_name",
        "active",
        "email_opted_in",
        "is_service_account",
    ]

    name: str = Field(
        title="The unique username for the account.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    def get_hydrated_version(self) -> "UserResponse":
        """Get the hydrated version of this user.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_user(self.id)

    # Body and metadata properties
    @property
    def active(self) -> bool:
        """The `active` property.

        Returns:
            the value of the property.
        """
        return self.get_body().active

    @property
    def activation_token(self) -> Optional[str]:
        """The `activation_token` property.

        Returns:
            the value of the property.
        """
        return self.get_body().activation_token

    @property
    def full_name(self) -> str:
        """The `full_name` property.

        Returns:
            the value of the property.
        """
        return self.get_body().full_name

    @property
    def email_opted_in(self) -> Optional[bool]:
        """The `email_opted_in` property.

        Returns:
            the value of the property.
        """
        return self.get_body().email_opted_in

    @property
    def is_service_account(self) -> bool:
        """The `is_service_account` property.

        Returns:
            the value of the property.
        """
        return self.get_body().is_service_account

    @property
    def is_admin(self) -> bool:
        """The `is_admin` property.

        Returns:
            Whether the user is an admin.
        """
        return self.get_body().is_admin

    @property
    def email(self) -> Optional[str]:
        """The `email` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().email

    @property
    def external_user_id(self) -> Optional[UUID]:
        """The `external_user_id` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().external_user_id

    @property
    def user_metadata(self) -> Dict[str, Any]:
        """The `user_metadata` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().user_metadata

    # Helper methods
    @classmethod
    def _get_crypt_context(cls) -> "CryptContext":
        """Returns the password encryption context.

        Returns:
            The password encryption context.
        """
        from passlib.context import CryptContext

        return CryptContext(schemes=["bcrypt"], deprecated="auto")

`activation_token` `property`

The activation_token property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`active` `property`

The active property.

Returns:

Type	Description
`bool`	the value of the property.

`email` `property`

The email property.

Returns:

Type	Description
`Optional[str]`	the value of the property.

`email_opted_in` `property`

The email_opted_in property.

Returns:

Type	Description
`Optional[bool]`	the value of the property.

`external_user_id` `property`

The external_user_id property.

Returns:

Type	Description
`Optional[UUID]`	the value of the property.

`full_name` `property`

The full_name property.

Returns:

Type	Description
`str`	the value of the property.

`is_admin` `property`

The is_admin property.

Returns:

Type	Description
`bool`	Whether the user is an admin.

`is_service_account` `property`

The is_service_account property.

Returns:

Type	Description
`bool`	the value of the property.

`user_metadata` `property`

The user_metadata property.

Returns:

Type	Description
`Dict[str, Any]`	the value of the property.

`get_hydrated_version()`

Get the hydrated version of this user.

Returns:

Type	Description
`UserResponse`	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/user.py

def get_hydrated_version(self) -> "UserResponse":
    """Get the hydrated version of this user.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_user(self.id)

`UserResponseBody`

Response body for users.

Source code in src/zenml/models/v2/core/user.py

class UserResponseBody(BaseDatedResponseBody):
    """Response body for users."""

    active: bool = Field(default=False, title="Whether the account is active.")
    activation_token: Optional[str] = Field(
        default=None,
        max_length=STR_FIELD_MAX_LENGTH,
        title="The activation token for the user. Only relevant for user "
        "accounts.",
    )
    full_name: str = Field(
        default="",
        title="The full name for the account owner. Only relevant for user "
        "accounts.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    email_opted_in: Optional[bool] = Field(
        default=None,
        title="Whether the user agreed to share their email. Only relevant for "
        "user accounts",
        description="`null` if not answered, `true` if agreed, "
        "`false` if skipped.",
    )
    is_service_account: bool = Field(
        title="Indicates whether this is a service account or a user account."
    )
    is_admin: bool = Field(
        title="Whether the account is an administrator.",
    )

`UserResponseMetadata`

Bases: BaseResponseMetadata

Response metadata for users.

Source code in src/zenml/models/v2/core/user.py

class UserResponseMetadata(BaseResponseMetadata):
    """Response metadata for users."""

    email: Optional[str] = Field(
        default="",
        title="The email address associated with the account. Only relevant "
        "for user accounts.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    external_user_id: Optional[UUID] = Field(
        default=None,
        title="The external user ID associated with the account. Only relevant "
        "for user accounts.",
    )
    user_metadata: Dict[str, Any] = Field(
        default={},
        title="The metadata associated with the user.",
    )

`UserScopedFilter`

Bases: BaseFilter

Model to enable advanced user-based scoping.

Source code in src/zenml/models/v2/base/scoped.py

class UserScopedFilter(BaseFilter):
    """Model to enable advanced user-based scoping."""

    FILTER_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        *BaseFilter.FILTER_EXCLUDE_FIELDS,
        "user",
        "scope_user",
    ]
    CLI_EXCLUDE_FIELDS: ClassVar[List[str]] = [
        *BaseFilter.CLI_EXCLUDE_FIELDS,
        "user_id",
        "scope_user",
    ]
    CUSTOM_SORTING_OPTIONS: ClassVar[List[str]] = [
        *BaseFilter.CUSTOM_SORTING_OPTIONS,
        "user",
    ]

    scope_user: Optional[UUID] = Field(
        default=None,
        description="The user to scope this query to.",
    )
    user_id: Optional[Union[UUID, str]] = Field(
        default=None,
        description="UUID of the user that created the entity.",
        union_mode="left_to_right",
    )
    user: Optional[Union[UUID, str]] = Field(
        default=None,
        description="Name/ID of the user that created the entity.",
    )

    def set_scope_user(self, user_id: UUID) -> None:
        """Set the user that is performing the filtering to scope the response.

        Args:
            user_id: The user ID to scope the response to.
        """
        self.scope_user = user_id

    def get_custom_filters(
        self, table: Type["AnySchema"]
    ) -> List["ColumnElement[bool]"]:
        """Get custom filters.

        Args:
            table: The query table.

        Returns:
            A list of custom filters.
        """
        custom_filters = super().get_custom_filters(table)

        from sqlmodel import and_

        from zenml.zen_stores.schemas import UserSchema

        if self.user:
            user_filter = and_(
                getattr(table, "user_id") == UserSchema.id,
                self.generate_name_or_id_query_conditions(
                    value=self.user,
                    table=UserSchema,
                    additional_columns=["full_name"],
                ),
            )
            custom_filters.append(user_filter)

        return custom_filters

    def apply_sorting(
        self,
        query: AnyQuery,
        table: Type["AnySchema"],
    ) -> AnyQuery:
        """Apply sorting to the query.

        Args:
            query: The query to which to apply the sorting.
            table: The query table.

        Returns:
            The query with sorting applied.
        """
        from sqlmodel import asc, desc

        from zenml.enums import SorterOps
        from zenml.zen_stores.schemas import UserSchema

        sort_by, operand = self.sorting_params

        if sort_by == "user":
            column = UserSchema.name

            query = query.outerjoin(
                UserSchema,
                getattr(table, "user_id") == UserSchema.id,
            )

            query = query.add_columns(UserSchema.name)

            if operand == SorterOps.ASCENDING:
                query = query.order_by(asc(column))
            else:
                query = query.order_by(desc(column))

            return query

        return super().apply_sorting(query=query, table=table)

    def apply_filter(
        self,
        query: AnyQuery,
        table: Type["AnySchema"],
    ) -> AnyQuery:
        """Applies the filter to a query.

        Args:
            query: The query to which to apply the filter.
            table: The query table.

        Returns:
            The query with filter applied.
        """
        query = super().apply_filter(query=query, table=table)

        if self.scope_user:
            query = query.where(getattr(table, "user_id") == self.scope_user)

        return query

`apply_filter(query, table)`

Applies the filter to a query.

Parameters:

Name	Type	Description	Default
`query`	`AnyQuery`	The query to which to apply the filter.	required
`table`	`Type[AnySchema]`	The query table.	required

Returns:

Type	Description
`AnyQuery`	The query with filter applied.

Source code in src/zenml/models/v2/base/scoped.py

def apply_filter(
    self,
    query: AnyQuery,
    table: Type["AnySchema"],
) -> AnyQuery:
    """Applies the filter to a query.

    Args:
        query: The query to which to apply the filter.
        table: The query table.

    Returns:
        The query with filter applied.
    """
    query = super().apply_filter(query=query, table=table)

    if self.scope_user:
        query = query.where(getattr(table, "user_id") == self.scope_user)

    return query

`apply_sorting(query, table)`

Apply sorting to the query.

Parameters:

Name	Type	Description	Default
`query`	`AnyQuery`	The query to which to apply the sorting.	required
`table`	`Type[AnySchema]`	The query table.	required

Returns:

Type	Description
`AnyQuery`	The query with sorting applied.

Source code in src/zenml/models/v2/base/scoped.py

def apply_sorting(
    self,
    query: AnyQuery,
    table: Type["AnySchema"],
) -> AnyQuery:
    """Apply sorting to the query.

    Args:
        query: The query to which to apply the sorting.
        table: The query table.

    Returns:
        The query with sorting applied.
    """
    from sqlmodel import asc, desc

    from zenml.enums import SorterOps
    from zenml.zen_stores.schemas import UserSchema

    sort_by, operand = self.sorting_params

    if sort_by == "user":
        column = UserSchema.name

        query = query.outerjoin(
            UserSchema,
            getattr(table, "user_id") == UserSchema.id,
        )

        query = query.add_columns(UserSchema.name)

        if operand == SorterOps.ASCENDING:
            query = query.order_by(asc(column))
        else:
            query = query.order_by(desc(column))

        return query

    return super().apply_sorting(query=query, table=table)

`get_custom_filters(table)`

Get custom filters.

Parameters:

Name	Type	Description	Default
`table`	`Type[AnySchema]`	The query table.	required

Returns:

Type	Description
`List[ColumnElement[bool]]`	A list of custom filters.

Source code in src/zenml/models/v2/base/scoped.py

def get_custom_filters(
    self, table: Type["AnySchema"]
) -> List["ColumnElement[bool]"]:
    """Get custom filters.

    Args:
        table: The query table.

    Returns:
        A list of custom filters.
    """
    custom_filters = super().get_custom_filters(table)

    from sqlmodel import and_

    from zenml.zen_stores.schemas import UserSchema

    if self.user:
        user_filter = and_(
            getattr(table, "user_id") == UserSchema.id,
            self.generate_name_or_id_query_conditions(
                value=self.user,
                table=UserSchema,
                additional_columns=["full_name"],
            ),
        )
        custom_filters.append(user_filter)

    return custom_filters

`set_scope_user(user_id)`

Set the user that is performing the filtering to scope the response.

Parameters:

Name	Type	Description	Default
`user_id`	`UUID`	The user ID to scope the response to.	required

Source code in src/zenml/models/v2/base/scoped.py

def set_scope_user(self, user_id: UUID) -> None:
    """Set the user that is performing the filtering to scope the response.

    Args:
        user_id: The user ID to scope the response to.
    """
    self.scope_user = user_id

`UserScopedRequest`

Bases: BaseRequest

Base user-owned request model.

Used as a base class for all domain models that are "owned" by a user.

Source code in src/zenml/models/v2/base/scoped.py

class UserScopedRequest(BaseRequest):
    """Base user-owned request model.

    Used as a base class for all domain models that are "owned" by a user.
    """

    user: UUID = Field(title="The id of the user that created this resource.")

    def get_analytics_metadata(self) -> Dict[str, Any]:
        """Fetches the analytics metadata for user scoped models.

        Returns:
            The analytics metadata.
        """
        metadata = super().get_analytics_metadata()
        metadata["user_id"] = self.user
        return metadata

`get_analytics_metadata()`

Fetches the analytics metadata for user scoped models.

Returns:

Type	Description
`Dict[str, Any]`	The analytics metadata.

Source code in src/zenml/models/v2/base/scoped.py

def get_analytics_metadata(self) -> Dict[str, Any]:
    """Fetches the analytics metadata for user scoped models.

    Returns:
        The analytics metadata.
    """
    metadata = super().get_analytics_metadata()
    metadata["user_id"] = self.user
    return metadata

`UserScopedResponse`

Bases: BaseIdentifiedResponse[UserBody, UserMetadata, UserResources], Generic[UserBody, UserMetadata, UserResources]

Base user-owned model.

Used as a base class for all domain models that are "owned" by a user.

Source code in src/zenml/models/v2/base/scoped.py

class UserScopedResponse(
    BaseIdentifiedResponse[UserBody, UserMetadata, UserResources],
    Generic[UserBody, UserMetadata, UserResources],
):
    """Base user-owned model.

    Used as a base class for all domain models that are "owned" by a user.
    """

    # Analytics
    def get_analytics_metadata(self) -> Dict[str, Any]:
        """Fetches the analytics metadata for user scoped models.

        Returns:
            The analytics metadata.
        """
        metadata = super().get_analytics_metadata()
        if self.user is not None:
            metadata["user_id"] = self.user.id
        return metadata

    # Body and metadata properties
    @property
    def user(self) -> Optional["UserResponse"]:
        """The `user` property.

        Returns:
            the value of the property.
        """
        return self.get_body().user

`user` `property`

The user property.

Returns:

Type	Description
`Optional[UserResponse]`	the value of the property.

`get_analytics_metadata()`

Fetches the analytics metadata for user scoped models.

Returns:

Type	Description
`Dict[str, Any]`	The analytics metadata.

Source code in src/zenml/models/v2/base/scoped.py

def get_analytics_metadata(self) -> Dict[str, Any]:
    """Fetches the analytics metadata for user scoped models.

    Returns:
        The analytics metadata.
    """
    metadata = super().get_analytics_metadata()
    if self.user is not None:
        metadata["user_id"] = self.user.id
    return metadata

`UserScopedResponseBody`

Base user-owned body.

Source code in src/zenml/models/v2/base/scoped.py

class UserScopedResponseBody(BaseDatedResponseBody):
    """Base user-owned body."""

    user: Optional["UserResponse"] = Field(
        title="The user who created this resource.", default=None
    )

`UserScopedResponseMetadata`

Bases: BaseResponseMetadata

Base user-owned metadata.

Source code in src/zenml/models/v2/base/scoped.py

class UserScopedResponseMetadata(BaseResponseMetadata):
    """Base user-owned metadata."""

`UserUpdate`

Bases: UserBase, BaseZenModel

Update model for users.

Source code in src/zenml/models/v2/core/user.py

class UserUpdate(UserBase, BaseZenModel):
    """Update model for users."""

    name: Optional[str] = Field(
        title="The unique username for the account.",
        max_length=STR_FIELD_MAX_LENGTH,
        default=None,
    )
    full_name: Optional[str] = Field(
        default=None,
        title="The full name for the account owner. Only relevant for user "
        "accounts.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    is_admin: Optional[bool] = Field(
        default=None,
        title="Whether the account is an administrator.",
    )
    active: Optional[bool] = Field(
        default=None, title="Whether the account is active."
    )
    old_password: Optional[str] = Field(
        default=None,
        title="The previous password for the user. Only relevant for user "
        "accounts. Required when updating the password.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    @model_validator(mode="after")
    def user_email_updates(self) -> "UserUpdate":
        """Validate that the UserUpdateModel conforms to the email-opt-in-flow.

        Returns:
            The validated values.

        Raises:
            ValueError: If the email was not provided when the email_opted_in
                field was set to True.
        """
        # When someone sets the email, or updates the email and hasn't
        #  before explicitly opted out, they are opted in
        if self.email is not None:
            if self.email_opted_in is None:
                self.email_opted_in = True

        # It should not be possible to do opt in without an email
        if self.email_opted_in is True:
            if self.email is None:
                raise ValueError(
                    "Please provide an email, when you are opting-in with "
                    "your email."
                )
        return self

    def create_copy(self, exclude: AbstractSet[str]) -> "UserUpdate":
        """Create a copy of the current instance.

        Args:
            exclude: Fields to exclude from the copy.

        Returns:
            A copy of the current instance.
        """
        return UserUpdate(
            **self.model_dump(
                exclude=set(exclude),
                exclude_unset=True,
            )
        )

`create_copy(exclude)`

Create a copy of the current instance.

Parameters:

Name	Type	Description	Default
`exclude`	`AbstractSet[str]`	Fields to exclude from the copy.	required

Returns:

Type	Description
`UserUpdate`	A copy of the current instance.

Source code in src/zenml/models/v2/core/user.py

def create_copy(self, exclude: AbstractSet[str]) -> "UserUpdate":
    """Create a copy of the current instance.

    Args:
        exclude: Fields to exclude from the copy.

    Returns:
        A copy of the current instance.
    """
    return UserUpdate(
        **self.model_dump(
            exclude=set(exclude),
            exclude_unset=True,
        )
    )

`user_email_updates()`

Validate that the UserUpdateModel conforms to the email-opt-in-flow.

Returns:

Type	Description
`UserUpdate`	The validated values.

Raises:

Type	Description
`ValueError`	If the email was not provided when the email_opted_in field was set to True.

Source code in src/zenml/models/v2/core/user.py

@model_validator(mode="after")
def user_email_updates(self) -> "UserUpdate":
    """Validate that the UserUpdateModel conforms to the email-opt-in-flow.

    Returns:
        The validated values.

    Raises:
        ValueError: If the email was not provided when the email_opted_in
            field was set to True.
    """
    # When someone sets the email, or updates the email and hasn't
    #  before explicitly opted out, they are opted in
    if self.email is not None:
        if self.email_opted_in is None:
            self.email_opted_in = True

    # It should not be possible to do opt in without an email
    if self.email_opted_in is True:
        if self.email is None:
            raise ValueError(
                "Please provide an email, when you are opting-in with "
                "your email."
            )
    return self

`WorkspaceFilter`

Bases: BaseFilter

Model to enable advanced filtering of all Workspaces.

Source code in src/zenml/models/v2/core/workspace.py

class WorkspaceFilter(BaseFilter):
    """Model to enable advanced filtering of all Workspaces."""

    name: Optional[str] = Field(
        default=None,
        description="Name of the workspace",
    )

`WorkspaceRequest`

Bases: BaseRequest

Request model for workspaces.

Source code in src/zenml/models/v2/core/workspace.py

class WorkspaceRequest(BaseRequest):
    """Request model for workspaces."""

    name: str = Field(
        title="The unique name of the workspace.",
        max_length=STR_FIELD_MAX_LENGTH,
    )
    description: str = Field(
        default="",
        title="The description of the workspace.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

`WorkspaceResponse`

Bases: BaseIdentifiedResponse[WorkspaceResponseBody, WorkspaceResponseMetadata, WorkspaceResponseResources]

Response model for workspaces.

Source code in src/zenml/models/v2/core/workspace.py

class WorkspaceResponse(
    BaseIdentifiedResponse[
        WorkspaceResponseBody,
        WorkspaceResponseMetadata,
        WorkspaceResponseResources,
    ]
):
    """Response model for workspaces."""

    name: str = Field(
        title="The unique name of the workspace.",
        max_length=STR_FIELD_MAX_LENGTH,
    )

    def get_hydrated_version(self) -> "WorkspaceResponse":
        """Get the hydrated version of this workspace.

        Returns:
            an instance of the same entity with the metadata field attached.
        """
        from zenml.client import Client

        return Client().zen_store.get_workspace(self.id)

    # Body and metadata properties
    @property
    def description(self) -> str:
        """The `description` property.

        Returns:
            the value of the property.
        """
        return self.get_metadata().description

`description` `property`

The description property.

Returns:

Type	Description
`str`	the value of the property.

`get_hydrated_version()`

Get the hydrated version of this workspace.

Returns:

Type	Description
`WorkspaceResponse`	an instance of the same entity with the metadata field attached.

Source code in src/zenml/models/v2/core/workspace.py

def get_hydrated_version(self) -> "WorkspaceResponse":
    """Get the hydrated version of this workspace.

    Returns:
        an instance of the same entity with the metadata field attached.
    """
    from zenml.client import Client

    return Client().zen_store.get_workspace(self.id)

`WorkspaceResponseBody`