Spaces

class SpacesClient(*, sdk_config: SDKConfiguration, generated_client: ApiClient)

Bases: object

Client for managing Arize spaces.

This class is primarily intended for internal use within the SDK. Users are highly encouraged to access resource-specific functionality via arize.ArizeClient.

The spaces client is a thin wrapper around the generated REST API client, using the shared generated API client owned by arize.config.SDKConfiguration.

Parameters:

• sdk_config (SDKConfiguration) – Resolved SDK configuration.

• generated_client (ApiClient) – Shared generated API client instance.

list(*, organization_id: str | None = None, name: str | None = None, limit: int = DEFAULT_LIST_LIMIT, cursor: str | None = None) → SpaceListResponse

List spaces the user has access to.

This endpoint supports cursor-based pagination. When provided, organization_id filters results to a particular organization. name filters results to spaces whose name contains the given substring (case-insensitive).

Parameters:

• organization_id (str | None) – Optional organization ID to filter results.

• name (str | None) – Optional case-insensitive substring filter on space name.

• limit (int) – Maximum number of spaces to return. The server may enforce an upper bound.

• cursor (str | None) – Opaque pagination cursor from a previous response.

Returns:

A paginated space list response from the Arize REST API.

Raises:

ApiException – If the API request fails.

Return type:

SpaceListResponse

get(*, space: str) → Space

Get a space by ID or name.

Parameters:

space (str) – Space ID or name to retrieve.

Returns:

The space object.

Raises:

ApiException – If the API request fails (for example, space not found).

Return type:

Space

create(*, name: str, organization_id: str, description: str | None = None) → Space

Create a new space.

Space names must be unique within the target organization.

Parameters:

• name (str) – Space name (must be unique within organization_id).

• organization_id (str) – Organization ID to create the space in.

• description (str | None) – Optional description of the space’s purpose.

Returns:

The created space object.

Raises:

ApiException – If the API request fails.

Return type:

Space

delete(*, space: str) → None

Delete a space by ID or name.

This operation is irreversible. It deletes the space and all resources that belong to it (models, monitors, dashboards, datasets, custom metrics, etc).

Parameters:

space (str) – Space ID or name to delete.

Returns:

This method returns None on success (204 No Content response).

Raises:

ApiException – If the API request fails (for example, space not found or insufficient permissions).

Return type:

None

update(*, space: str, name: str | None = None, description: str | None = None) → Space

Update a space by ID or name.

Parameters:

• space (str) – Space ID or name to update.

• name (str | None) – Updated name for the space.

• description (str | None) – Updated description for the space.

Returns:

The updated space object.

Raises:

• ValueError – If neither name nor description is provided.

• ApiException – If the API request fails (for example, space not found or insufficient permissions).

Return type:

Space

add_user(*, space: str, user_id: str, role: PredefinedSpaceRole | CustomSpaceRole) → SpaceMembership

Add a user to a space (or update their role if already a member).

If the user is already a member of the space, their role is updated to the specified value (upsert). The user must already be a member of the space’s parent organization — auto-enrollment is not performed.

Role constraints

• Users with an annotator account role can only be assigned the annotator predefined space role.

• Users with a non-annotator account role cannot be assigned the annotator predefined space role.

Requires space admin role for predefined roles, or ROLE_BINDING_CREATE permission for custom roles.

Parameters:

• space (str) – Space ID or name.

• user_id (str) – Global ID of the user to add.

• role (PredefinedSpaceRole | CustomSpaceRole) – Role assignment for the user. Use PredefinedSpaceRole(name="<role>") for predefined roles (admin, member, read-only, annotator), or CustomRoleAssignment(type="custom", id="<role_id>") for a custom RBAC role.

Returns:

The created or updated space membership record.

Raises:

ApiException – If the API request fails (for example, space or user not found, user not in the parent organization, role constraint violation, or insufficient permissions).

Return type:

SpaceMembership

remove_user(*, space: str, user_id: str) → None

Remove a user from a space.

Removes both the legacy space-membership row and any RBAC role bindings for the user on this space.

Requires space admin role (legacy auth) or ROLE_BINDING_DELETE permission (RBAC).

Parameters:

• space (str) – Space ID or name.

• user_id (str) – Global ID of the user to remove.

Returns:

This method returns None on success (204 No Content response).

Raises:

ApiException – If the API request fails (for example, space or user not found, or user is not a member of the space).

Return type:

None

Response Types

class CustomSpaceRole(*, type: Literal['custom'] = 'custom', id: Annotated[str, Strict(strict=True)], name: Annotated[str, Strict(strict=True)] | None = None)

Bases: CustomRoleAssignment

A custom RBAC role assignment for a space.

The type discriminator is set to "custom" automatically.

Parameters:

• id (Annotated[str, Strict(strict=True)]) – The unique identifier of the custom RBAC role.

• name (Annotated[str, Strict(strict=True)] | None) – Human-readable name of the custom role (returned in responses only; ignored on input).

• type (Literal['custom'])

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

type: Literal['custom']

model_config: ClassVar[ConfigDict] = {'populate_by_name': True, 'protected_namespaces': (), 'validate_assignment': True, 'validate_by_alias': True, 'validate_by_name': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class PredefinedSpaceRole(*, type: Literal['predefined'] = 'predefined', name: UserSpaceRole)

Bases: PredefinedRoleAssignment

A predefined space role assignment.

The type discriminator is set to "predefined" automatically.

Parameters:

• name (UserSpaceRole) – The predefined role name ("admin", "member", "read-only", or "annotator").

• type (Literal['predefined'])

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

type: Literal['predefined']

model_config: ClassVar[ConfigDict] = {'populate_by_name': True, 'protected_namespaces': (), 'validate_assignment': True, 'validate_by_alias': True, 'validate_by_name': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class Space(*, id: Annotated[str, Strict(strict=True)], name: Annotated[str, Strict(strict=True)], description: Annotated[str, Strict(strict=True)], created_at: datetime)

Bases: BaseModel

A space is a container within an organization for grouping related projects, datasets, and experiments. Spaces enable team collaboration or isolated experimentation with role-based access control.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

• id (Annotated[str, Strict(strict=True)])

• name (Annotated[str, Strict(strict=True)])

• description (Annotated[str, Strict(strict=True)])

• created_at (datetime)

id: StrictStr

name: StrictStr

description: StrictStr

created_at: datetime

model_config: ClassVar[ConfigDict] = {'populate_by_name': True, 'protected_namespaces': (), 'validate_assignment': True, 'validate_by_alias': True, 'validate_by_name': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

to_str() → str

Returns the string representation of the model using alias

Return type:

str

to_json() → str

Returns the JSON representation of the model using alias

Return type:

str

classmethod from_json(json_str: str) → Self | None

Create an instance of Space from a JSON string

Parameters:

json_str (str)

Return type:

Self | None

to_dict() → Dict[str, Any]

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

• None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

Return type:

Dict[str, Any]

classmethod from_dict(obj: Dict[str, Any] | None) → Self | None

Create an instance of Space from a dict

Parameters:

obj (Dict[str, Any] | None)

Return type:

Self | None

class SpaceListResponse(*, spaces: List[Space], pagination: PaginationMetadata)

Bases: BaseModel

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

• spaces (List[Space])

• pagination (PaginationMetadata)

spaces: List[Space]

pagination: PaginationMetadata

model_config: ClassVar[ConfigDict] = {'populate_by_name': True, 'protected_namespaces': (), 'validate_assignment': True, 'validate_by_alias': True, 'validate_by_name': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

to_str() → str

Returns the string representation of the model using alias

Return type:

str

to_json() → str

Returns the JSON representation of the model using alias

Return type:

str

classmethod from_json(json_str: str) → Self | None

Create an instance of SpaceListResponse from a JSON string

Parameters:

json_str (str)

Return type:

Self | None

to_dict() → Dict[str, Any]

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

• None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

Return type:

Dict[str, Any]

classmethod from_dict(obj: Dict[str, Any] | None) → Self | None

Create an instance of SpaceListResponse from a dict

Parameters:

obj (Dict[str, Any] | None)

Return type:

Self | None

to_df(by_alias: bool = False, exclude_none: str | bool = True, json_normalize: bool = False, convert_dtypes: bool = True, expand_field: str = 'additional_properties', expand_prefix: str = '') → pd.DataFrame

Convert a list of objects to a pandas.DataFrame.

Behavior:

• If an item is a Pydantic v2 model, use .model_dump(by_alias=…).

• If an item is a mapping (dict-like), use it as-is.

• Otherwise, raise a ValueError (unsupported row type).

Parameters:

• self (object) – The object instance containing the field to convert.

• by_alias (bool) – Use field aliases when dumping Pydantic models.

• exclude_none (str | bool) – Control None/NaN column dropping. - False: keep Nones as-is - “all”: drop columns where all values are None/NaN - “any”: drop columns where any value is None/NaN - True: alias for “all”

• json_normalize (bool) – If True, flatten nested dicts via pandas.json_normalize.

• convert_dtypes (bool) – If True, call DataFrame.convert_dtypes() at the end.

• expand_field (str) – If set, look for this field in each row and expand its keys into top-level columns.

• expand_prefix (str) – If set, prefix expanded column names with this string.

Returns:

The converted DataFrame.

Return type:

pandas.DataFrame

class SpaceMembership(*, id: str, user_id: str, space_id: str, role: PredefinedSpaceRole | CustomSpaceRole)

Bases: BaseModel

A space membership record with domain-typed role.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

• id (str)

• user_id (str)

• space_id (str)

• role (Annotated[PredefinedSpaceRole | CustomSpaceRole, FieldInfo(annotation=NoneType, required=True, discriminator='type')])

id: str

user_id: str

space_id: str

role: Annotated[PredefinedSpaceRole | CustomSpaceRole, FieldInfo(annotation=NoneType, required=True, discriminator='type')]

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class SpaceMembershipInput(*, user_id: Annotated[str, Strict(strict=True)], role: SpaceRoleAssignment)

Bases: BaseModel

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

• user_id (Annotated[str, Strict(strict=True)])

• role (SpaceRoleAssignment)

user_id: StrictStr

role: SpaceRoleAssignment

model_config: ClassVar[ConfigDict] = {'populate_by_name': True, 'protected_namespaces': (), 'validate_assignment': True, 'validate_by_alias': True, 'validate_by_name': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

to_str() → str

Returns the string representation of the model using alias

Return type:

str

to_json() → str

Returns the JSON representation of the model using alias

Return type:

str

classmethod from_json(json_str: str) → Self | None

Create an instance of SpaceMembershipInput from a JSON string

Parameters:

json_str (str)

Return type:

Self | None

to_dict() → Dict[str, Any]

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

• None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

Return type:

Dict[str, Any]

classmethod from_dict(obj: Dict[str, Any] | None) → Self | None

Create an instance of SpaceMembershipInput from a dict

Parameters:

obj (Dict[str, Any] | None)

Return type:

Self | None

class SpaceRoleAssignment(*args, oneof_schema_1_validator: PredefinedRoleAssignment | None = None, oneof_schema_2_validator: CustomRoleAssignment | None = None, actual_instance: CustomRoleAssignment | PredefinedRoleAssignment | None = None, one_of_schemas: Set[str] = {'CustomRoleAssignment', 'PredefinedRoleAssignment'}, discriminator_value_class_map: Dict[str, str] = {})

Bases: BaseModel

A role assignment for a space membership. Discriminated by type: - predefined: one of the predefined roles (admin, member, read-only, annotator) - custom: a custom RBAC role identified by its ID

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

• oneof_schema_1_validator (PredefinedRoleAssignment | None)

• oneof_schema_2_validator (CustomRoleAssignment | None)

• actual_instance (CustomRoleAssignment | PredefinedRoleAssignment | None)

• one_of_schemas (Set[str])

• discriminator_value_class_map (Dict[str, str])

oneof_schema_1_validator: PredefinedRoleAssignment | None

oneof_schema_2_validator: CustomRoleAssignment | None

actual_instance: CustomRoleAssignment | PredefinedRoleAssignment | None

one_of_schemas: Set[str]

model_config: ClassVar[ConfigDict] = {'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

discriminator_value_class_map: Dict[str, str]

classmethod actual_instance_must_validate_oneof(v)

classmethod from_dict(obj: str | Dict[str, Any]) → Self

Parameters:

obj (str | Dict[str, Any])

Return type:

Self

classmethod from_json(json_str: str) → Self

Returns the object represented by the json string

Parameters:

json_str (str)

Return type:

Self

to_json() → str

Returns the JSON representation of the actual instance

Return type:

str

to_dict() → Dict[str, Any] | CustomRoleAssignment | PredefinedRoleAssignment | None

Returns the dict representation of the actual instance

Return type:

Dict[str, Any] | CustomRoleAssignment | PredefinedRoleAssignment | None

to_str() → str

Returns the string representation of the actual instance

Return type:

str

class UserSpaceRole(value, names=_not_given, *values, module=None, qualname=None, type=None, start=1, boundary=None)

Bases: str, Enum

Space-level role for the user. - admin: Full access to the space and its resources. - member: Standard access to the space. - read-only: Read-only access to the space. - annotator: Limited access for annotation tasks only.

ADMIN = 'admin'

MEMBER = 'member'

READ_MINUS_ONLY = 'read-only'

ANNOTATOR = 'annotator'

classmethod from_json(json_str: str) → Self

Create an instance of UserSpaceRole from a JSON string

Parameters:

json_str (str)

Return type:

Self