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xcc.Settings

class Settings(_case_sensitive: bool | None = None, _env_prefix: str | None = None, _env_file: DotenvType | None = PosixPath('.'), _env_file_encoding: str | None = None, _env_nested_delimiter: str | None = None, _secrets_dir: str | Path | None = None, *, REFRESH_TOKEN: Optional[str] = None, ACCESS_TOKEN: Optional[str] = None, HOST: str = 'platform.xanadu.ai', PORT: int = 443, TLS: bool = True)[source]

Bases: pydantic_settings.main.BaseSettings

Represents the configuration for connecting to the Xanadu Cloud.

The location where this configuration is saved depends on the current operating system. Specifically,

  • Windows: C:\Users\%USERNAME%\AppData\Local\Xanadu\xanadu-cloud\.env

  • MacOS: /home/$USER/Library/Application\ Support/xanadu-cloud/.env

  • Linux: /home/$USER/.config/xanadu-cloud/.env

Example:

The following example shows how to use the Settings class to load and save a Xanadu Cloud configuration. To begin, loading a configuration is as simple as instantiating a settings object:

>>> import xcc
>>> settings = xcc.Settings()
>>> settings
Settings(REFRESH_TOKEN=None, ACCESS_TOKEN=None, HOST'platform.xanadu.ai', PORT=443, TLS=True)

Now, individual options can be accessed or assigned through their corresponding attribute:

>>> settings.PORT
443
>>> settings.PORT = 80
>>> settings.PORT
80

Note

Several aggregate representations of options are also available, such as

>>> settings.model_dump()
{'REFRESH_TOKEN': None, 'ACCESS_TOKEN': None, ..., 'TLS': True}
>>> settings.model_dump_json()
'{"REFRESH_TOKEN": null, "ACCESS_TOKEN": null, ..., "TLS": true}'

Finally, saving a configuration can be done by invoking Settings.save():

>>> settings.save()

model_computed_fields

Get the computed fields of this model instance.

model_config

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

model_extra

Get extra fields set during validation.

model_fields

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

model_fields_set

Returns the set of fields that have been explicitly set on this model instance.

REFRESH_TOKEN

JWT refresh token that can be used to fetch access tokens from the Xanadu Cloud.

ACCESS_TOKEN

JWT access token that can be used to authenticate requests to the Xanadu Cloud.

HOST

Hostname of the Xanadu Cloud server.

PORT

Port of the Xanadu Cloud server.

TLS

Whether to use HTTPS for requests to the Xanadu Cloud.
model_computed_fields

Get the computed fields of this model instance.

Returns

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[SettingsConfigDict] = {'arbitrary_types_allowed': True, 'case_sensitive': True, 'env_file': '/home/docs/.config/xanadu-cloud/.env', 'env_file_encoding': None, 'env_nested_delimiter': None, 'env_prefix': 'XANADU_CLOUD_', 'extra': 'forbid', 'protected_namespaces': ('model_', 'settings_'), 'secrets_dir': None, 'validate_default': True}

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

model_extra

Get extra fields set during validation.

Returns

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields: ClassVar[dict[str, FieldInfo]] = {'ACCESS_TOKEN': FieldInfo(annotation=Union[str, NoneType], required=False), 'HOST': FieldInfo(annotation=str, required=False, default='platform.xanadu.ai'), 'PORT': FieldInfo(annotation=int, required=False, default=443), 'REFRESH_TOKEN': FieldInfo(annotation=Union[str, NoneType], required=False), 'TLS': FieldInfo(annotation=bool, required=False, default=True)}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

model_fields_set

Returns the set of fields that have been explicitly set on this model instance.

Returns

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

REFRESH_TOKEN: Optional[str]

JWT refresh token that can be used to fetch access tokens from the Xanadu Cloud.

ACCESS_TOKEN: Optional[str]

JWT access token that can be used to authenticate requests to the Xanadu Cloud.

HOST: str

Hostname of the Xanadu Cloud server.

PORT: int

Port of the Xanadu Cloud server.

TLS: bool

Whether to use HTTPS for requests to the Xanadu Cloud.

construct([_fields_set])

copy(*[, include, exclude, update, deep])

Returns a copy of the model.

dict(*[, include, exclude, by_alias, ...])

from_orm(obj)

json(*[, include, exclude, by_alias, ...])

model_construct([_fields_set])

Creates a new instance of the Model class with validated data.

model_copy(*[, update, deep])

Usage docs: https://docs.pydantic.dev/2.5/concepts/serialization/#model_copy

model_dump(*[, mode, include, exclude, ...])

Usage docs: https://docs.pydantic.dev/2.5/concepts/serialization/#modelmodel_dump

model_dump_json(*[, indent, include, ...])

Usage docs: https://docs.pydantic.dev/2.5/concepts/serialization/#modelmodel_dump_json

model_json_schema([by_alias, ref_template, ...])

Generates a JSON schema for a model class.

model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

model_post_init(_BaseModel__context)

Override this method to perform additional initialization after __init__ and model_construct.

model_rebuild(*[, force, raise_errors, ...])

Try to rebuild the pydantic-core schema for the model.

model_validate(obj, *[, strict, ...])

Validate a pydantic model instance.

model_validate_json(json_data, *[, strict, ...])

Usage docs: https://docs.pydantic.dev/2.5/concepts/json/#json-parsing

model_validate_strings(obj, *[, strict, context])

Validate the given object contains string data against the Pydantic model.

parse_file(path, *[, content_type, ...])

parse_obj(obj)

parse_raw(b, *[, content_type, encoding, ...])

save()

Saves the current settings to the .env file.

schema([by_alias, ref_template])

schema_json(*[, by_alias, ref_template])

settings_customise_sources(settings_cls, ...)

Define the sources and their order for loading the settings values.

update_forward_refs(**localns)

validate(value)

classmethod construct(_fields_set: set[str] | None = None, **values: Any) → Model
copy(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, update: Dict[str, Any] | None = None, deep: bool = False) → Model

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters

  • include – Optional set or mapping specifying which fields to include in the copied model.

  • exclude – Optional set or mapping specifying which fields to exclude in the copied model.

  • update – Optional dictionary of field-value pairs to override field values in the copied model.

  • deep – If True, the values of fields that are Pydantic models will be deep copied.

Returns

A copy of the model with included, excluded and updated fields as specified.

dict(*, include: IncEx = None, exclude: IncEx = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → Dict[str, Any]
classmethod from_orm(obj: Any) → Model
json(*, include: IncEx = None, exclude: IncEx = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Callable[[Any], Any] | None = PydanticUndefined, models_as_dict: bool = PydanticUndefined, **dumps_kwargs: Any) → str
classmethod model_construct(_fields_set: set[str] | None = None, **values: Any) → Model

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

Parameters

  • _fields_set – The set of field names accepted for the Model instance.

  • values – Trusted or pre-validated data dictionary.

Returns

A new instance of the Model class with validated data.

model_copy(*, update: dict[str, Any] | None = None, deep: bool = False) → Model

Usage docs: https://docs.pydantic.dev/2.5/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters

  • update – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

  • deep – Set to True to make a deep copy of the model.

Returns

New model instance.

model_dump(*, mode: Literal['json', 'python'] | str = 'python', include: IncEx = None, exclude: IncEx = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, round_trip: bool = False, warnings: bool = True) → dict[str, Any]

Usage docs: https://docs.pydantic.dev/2.5/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters

  • mode – The mode in which to_python should run. If mode is ‘json’, the dictionary will only contain JSON serializable types. If mode is ‘python’, the dictionary may contain any Python objects.

  • include – A list of fields to include in the output.

  • exclude – A list of fields to exclude from the output.

  • by_alias – Whether to use the field’s alias in the dictionary key if defined.

  • exclude_unset – Whether to exclude fields that have not been explicitly set.

  • exclude_defaults – Whether to exclude fields that are set to their default value from the output.

  • exclude_none – Whether to exclude fields that have a value of None from the output.

  • round_trip – Whether to enable serialization and deserialization round-trip support.

  • warnings – Whether to log warnings when invalid fields are encountered.

Returns

A dictionary representation of the model.

model_dump_json(*, indent: int | None = None, include: IncEx = None, exclude: IncEx = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, round_trip: bool = False, warnings: bool = True) → str

Usage docs: https://docs.pydantic.dev/2.5/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters

  • indent – Indentation to use in the JSON output. If None is passed, the output will be compact.

  • include – Field(s) to include in the JSON output. Can take either a string or set of strings.

  • exclude – Field(s) to exclude from the JSON output. Can take either a string or set of strings.

  • by_alias – Whether to serialize using field aliases.

  • exclude_unset – Whether to exclude fields that have not been explicitly set.

  • exclude_defaults – Whether to exclude fields that have the default value.

  • exclude_none – Whether to exclude fields that have a value of None.

  • round_trip – Whether to use serialization/deserialization between JSON and class instance.

  • warnings – Whether to show any warnings that occurred during serialization.

Returns

A JSON string representation of the model.

classmethod model_json_schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}', schema_generator: type[GenerateJsonSchema] = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: JsonSchemaMode = 'validation') → dict[str, Any]

Generates a JSON schema for a model class.

Parameters

  • by_alias – Whether to use attribute aliases or not.

  • ref_template – The reference template.

  • schema_generator – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

  • mode – The mode in which to generate the schema.

Returns

The JSON schema for the given model class.

classmethod model_parametrized_name(params: tuple[type[Any], ...]) → str

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters

params – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Returns

String representing the new class where params are passed to cls as type variables.

Raises

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context: Any) → None

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

classmethod model_rebuild(*, force: bool = False, raise_errors: bool = True, _parent_namespace_depth: int = 2, _types_namespace: dict[str, Any] | None = None) → bool | None

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters

  • force – Whether to force the rebuilding of the model schema, defaults to False.

  • raise_errors – Whether to raise errors, defaults to True.

  • _parent_namespace_depth – The depth level of the parent namespace, defaults to 2.

  • _types_namespace – The types namespace, defaults to None.

Returns

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj: Any, *, strict: bool | None = None, from_attributes: bool | None = None, context: dict[str, Any] | None = None) → Model

Validate a pydantic model instance.

Parameters

  • obj – The object to validate.

  • strict – Whether to raise an exception on invalid fields.

  • from_attributes – Whether to extract data from object attributes.

  • context – Additional context to pass to the validator.

Raises

ValidationError – If the object could not be validated.

Returns

The validated model instance.

classmethod model_validate_json(json_data: str | bytes | bytearray, *, strict: bool | None = None, context: dict[str, Any] | None = None) → Model

Usage docs: https://docs.pydantic.dev/2.5/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters

  • json_data – The JSON data to validate.

  • strict – Whether to enforce types strictly.

  • context – Extra variables to pass to the validator.

Returns

The validated Pydantic model.

Raises

ValueError – If json_data is not a JSON string.

classmethod model_validate_strings(obj: Any, *, strict: bool | None = None, context: dict[str, Any] | None = None) → Model

Validate the given object contains string data against the Pydantic model.

Parameters

  • obj – The object contains string data to validate.

  • strict – Whether to enforce types strictly.

  • context – Extra variables to pass to the validator.

Returns

The validated Pydantic model.

classmethod parse_file(path: str | Path, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) → Model
classmethod parse_obj(obj: Any) → Model
classmethod parse_raw(b: str | bytes, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) → Model
save() → None[source]

Saves the current settings to the .env file.

classmethod schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}') → Dict[str, Any]
classmethod schema_json(*, by_alias: bool = True, ref_template: str = '#/$defs/{model}', **dumps_kwargs: Any) → str
classmethod settings_customise_sources(settings_cls: type[BaseSettings], init_settings: PydanticBaseSettingsSource, env_settings: PydanticBaseSettingsSource, dotenv_settings: PydanticBaseSettingsSource, file_secret_settings: PydanticBaseSettingsSource) → tuple[PydanticBaseSettingsSource, ...]

Define the sources and their order for loading the settings values.

Parameters

  • settings_cls – The Settings class.

  • init_settings – The InitSettingsSource instance.

  • env_settings – The EnvSettingsSource instance.

  • dotenv_settings – The DotEnvSettingsSource instance.

  • file_secret_settings – The SecretsSettingsSource instance.

Returns

A tuple containing the sources and their order for loading the settings values.

classmethod update_forward_refs(**localns: Any) → None
classmethod validate(value: Any) → Model
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