Skip to content

rg.Settings

rg.Settings is used to define the settings of an Argilla Dataset. The settings can be used to configure the behavior of the dataset, such as the fields, questions, guidelines, metadata, and vectors. The Settings class is passed to the Dataset class and used to create the dataset on the server. Once created, the settings of a dataset cannot be changed.

Usage Examples

Creating a new dataset with settings

To create a new dataset with settings, instantiate the Settings class and pass it to the Dataset class.

import argilla as rg

settings = rg.Settings(
    guidelines="Select the sentiment of the prompt.",
    fields=[rg.TextField(name="prompt", use_markdown=True)],
    questions=[rg.LabelQuestion(name="sentiment", labels=["positive", "negative"])],
)

dataset = rg.Dataset(name="sentiment_analysis", settings=settings)

# Create the dataset on the server
dataset.create()

To define the settings for fields, questions, metadata, vectors, or distribution, refer to the rg.TextField, rg.LabelQuestion, rg.TermsMetadataProperty, and rg.VectorField, rg.TaskDistribution class documentation.

Adding or removing properties to settings

The settings object can be modified before create the dataset by adding, replacing or removing properties by using the method settings.add and settings.<>.remove

import argilla as rg

settings = rg.Settings(
    guidelines="Select the sentiment of the prompt.",
    fields=[rg.TextField(name="prompt", use_markdown=True)],
    questions=[rg.LabelQuestion(name="sentiment", labels=["positive", "negative"])],
)

# Adding a new property
settings.add(rg.TextField(name="response", use_markdown=True))

# Replace an existing property by other property type
settings.add(rg.TextQuestion(name="response", use_markdown=False))

# Remove an existing property
settings.questions.remove("response")

Creating settings using built in templates

Argilla provides built-in templates for creating settings for common dataset types. To use a template, use the class methods of the Settings class. There are three built-in templates available for classification, ranking, and rating tasks. Template settings also include default guidelines and mappings.

Classification Task

You can define a classification task using the rg.Settings.for_classification class method. This will create a dataset with a text field and a label question. You can select field types using the field_type parameter with image or text.

settings = rg.Settings.for_classification(labels=["positive", "negative"]) # (1)

This will return a Settings object with the following settings:

settings = Settings(
    guidelines="Select a label for the document.",
    fields=[rg.TextField(field_type)(name="text")],
    questions=[LabelQuestion(name="label", labels=labels)],
    mapping={"input": "text", "output": "label", "document": "text"},
)

Ranking Task

You can define a ranking task using the rg.Settings.for_ranking class method. This will create a dataset with a text field and a ranking question.

settings = rg.Settings.for_ranking()

This will return a Settings object with the following settings:

settings = Settings(
    guidelines="Rank the responses.",
    fields=[
        rg.TextField(name="instruction"),
        rg.TextField(name="response1"),
        rg.TextField(name="response2"),
    ],
    questions=[RankingQuestion(name="ranking", values=["response1", "response2"])],
    mapping={
        "input": "instruction",
        "prompt": "instruction",
        "chosen": "response1",
        "rejected": "response2",
    },
)

Rating Task

You can define a rating task using the rg.Settings.for_rating class method. This will create a dataset with a text field and a rating question.

settings = rg.Settings.for_rating()

This will return a Settings object with the following settings:

settings = Settings(
    guidelines="Rate the response.",
    fields=[
        rg.TextField(name="instruction"),
        rg.TextField(name="response"),
    ],
    questions=[RatingQuestion(name="rating", values=[1, 2, 3, 4, 5])],
    mapping={
        "input": "instruction",
        "prompt": "instruction",
        "output": "response",
        "score": "rating",
    },
)

Settings

Bases: DefaultSettingsMixin, Resource

Settings class for Argilla Datasets.

This class is used to define the representation of a Dataset within the UI.

Source code in src/argilla/settings/_resource.py
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
class Settings(DefaultSettingsMixin, Resource):
    """
    Settings class for Argilla Datasets.

    This class is used to define the representation of a Dataset within the UI.
    """

    def __init__(
        self,
        fields: Optional[List[Field]] = None,
        questions: Optional[List[QuestionType]] = None,
        vectors: Optional[List[VectorField]] = None,
        metadata: Optional[List[MetadataType]] = None,
        guidelines: Optional[str] = None,
        allow_extra_metadata: bool = False,
        distribution: Optional[TaskDistribution] = None,
        mapping: Optional[Dict[str, Union[str, Sequence[str]]]] = None,
        _dataset: Optional["Dataset"] = None,
    ) -> None:
        """
        Args:
            fields (List[Field]): A list of Field objects that represent the fields in the Dataset.
            questions (List[Union[LabelQuestion, MultiLabelQuestion, RankingQuestion, TextQuestion, RatingQuestion]]):
                A list of Question objects that represent the questions in the Dataset.
            vectors (List[VectorField]): A list of VectorField objects that represent the vectors in the Dataset.
            metadata (List[MetadataField]): A list of MetadataField objects that represent the metadata in the Dataset.
            guidelines (str): A string containing the guidelines for the Dataset.
            allow_extra_metadata (bool): A boolean that determines whether or not extra metadata is allowed in the
                Dataset. Defaults to False.
            distribution (TaskDistribution): The annotation task distribution configuration.
                Default to DEFAULT_TASK_DISTRIBUTION
            mapping (Dict[str, Union[str, Sequence[str]]]): A dictionary that maps incoming data names to Argilla dataset attributes in DatasetRecords.
        """
        super().__init__(client=_dataset._client if _dataset else None)

        self._dataset = _dataset
        self._distribution = distribution or TaskDistribution.default()
        self._mapping = mapping
        self.__guidelines = self.__process_guidelines(guidelines)
        self.__allow_extra_metadata = allow_extra_metadata

        self.__questions = SettingsProperties(self, questions)
        self.__fields = SettingsProperties(self, fields)
        self.__vectors = SettingsProperties(self, vectors)
        self.__metadata = SettingsProperties(self, metadata)

    #####################
    # Properties        #
    #####################

    @property
    def fields(self) -> "SettingsProperties":
        return self.__fields

    @fields.setter
    def fields(self, fields: List[Field]):
        self.__fields = SettingsProperties(self, fields)

    @property
    def questions(self) -> "SettingsProperties":
        return self.__questions

    @questions.setter
    def questions(self, questions: List[QuestionType]):
        self.__questions = SettingsProperties(self, questions)

    @property
    def vectors(self) -> "SettingsProperties":
        return self.__vectors

    @vectors.setter
    def vectors(self, vectors: List[VectorField]):
        self.__vectors = SettingsProperties(self, vectors)

    @property
    def metadata(self) -> "SettingsProperties":
        return self.__metadata

    @metadata.setter
    def metadata(self, metadata: List[MetadataType]):
        self.__metadata = SettingsProperties(self, metadata)

    @property
    def guidelines(self) -> str:
        return self.__guidelines

    @guidelines.setter
    def guidelines(self, guidelines: str):
        self.__guidelines = self.__process_guidelines(guidelines)

    @property
    def allow_extra_metadata(self) -> bool:
        return self.__allow_extra_metadata

    @allow_extra_metadata.setter
    def allow_extra_metadata(self, value: bool):
        self.__allow_extra_metadata = value

    @property
    def distribution(self) -> TaskDistribution:
        return self._distribution

    @distribution.setter
    def distribution(self, value: TaskDistribution) -> None:
        self._distribution = value

    @property
    def mapping(self) -> Dict[str, Union[str, Sequence[str]]]:
        return self._mapping

    @mapping.setter
    def mapping(self, value: Dict[str, Union[str, Sequence[str]]]):
        self._mapping = value

    @property
    def dataset(self) -> "Dataset":
        return self._dataset

    @dataset.setter
    def dataset(self, dataset: "Dataset"):
        self._dataset = dataset
        self._client = dataset._client

    @cached_property
    def schema(self) -> dict:
        schema_dict = {}

        for field in self.fields:
            schema_dict[field.name] = field

        for question in self.questions:
            schema_dict[question.name] = question

        for vector in self.vectors:
            schema_dict[vector.name] = vector

        for metadata in self.metadata:
            schema_dict[metadata.name] = metadata

        return schema_dict

    @cached_property
    def schema_by_id(self) -> Dict[UUID, Union[Field, QuestionType, MetadataType, VectorField]]:
        return {v.id: v for v in self.schema.values()}

    def validate(self) -> None:
        self._validate_empty_settings()
        self._validate_duplicate_names()

        for field in self.fields:
            field.validate()

    #####################
    #  Public methods   #
    #####################

    def get(self) -> "Settings":
        self.fields = self._fetch_fields()
        self.questions = self._fetch_questions()
        self.vectors = self._fetch_vectors()
        self.metadata = self._fetch_metadata()
        self.__fetch_dataset_related_attributes()

        self._update_last_api_call()
        return self

    def create(self) -> "Settings":
        self.validate()

        self._update_dataset_related_attributes()
        self.__fields._create()
        self.__questions._create()
        self.__vectors._create()
        self.__metadata._create()

        self._update_last_api_call()
        return self

    def update(self) -> "Resource":
        self.validate()

        self._update_dataset_related_attributes()
        self.__fields._update()
        self.__questions._update()
        self.__vectors._update()
        self.__metadata._update()
        self.__questions._update()

        self._update_last_api_call()
        return self

    def serialize(self):
        try:
            return {
                "guidelines": self.guidelines,
                "questions": self.__questions.serialize(),
                "fields": self.__fields.serialize(),
                "vectors": self.vectors.serialize(),
                "metadata": self.metadata.serialize(),
                "allow_extra_metadata": self.allow_extra_metadata,
                "distribution": self.distribution.to_dict(),
                "mapping": self.mapping,
            }
        except Exception as e:
            raise ArgillaSerializeError(f"Failed to serialize the settings. {e.__class__.__name__}") from e

    def to_json(self, path: Union[Path, str]) -> None:
        """Save the settings to a file on disk

        Parameters:
            path (str): The path to save the settings to
        """
        if not isinstance(path, Path):
            path = Path(path)
        if path.exists():
            raise FileExistsError(f"File {path} already exists")
        with open(path, "w") as file:
            json.dump(self.serialize(), file)

    @classmethod
    def from_json(cls, path: Union[Path, str]) -> "Settings":
        """Load the settings from a file on disk"""

        with open(path, "r") as file:
            settings_dict = json.load(file)
            return cls._from_dict(settings_dict)

    @classmethod
    def from_hub(
        cls,
        repo_id: str,
        subset: Optional[str] = None,
        feature_mapping: Optional[Dict[str, Literal["question", "field", "metadata"]]] = None,
        **kwargs,
    ) -> "Settings":
        """Load the settings from the Hub

        Parameters:
            repo_id (str): The ID of the repository to load the settings from on the Hub.
            subset (Optional[str]): The subset of the repository to load the settings from.
            feature_mapping (Dict[str, Literal["question", "field", "metadata"]]): A dictionary that maps incoming column names to Argilla attributes.
        """

        settings = build_settings_from_repo_id(repo_id=repo_id, feature_mapping=feature_mapping, subset=subset)
        return settings

    def __eq__(self, other: "Settings") -> bool:
        if not (other and isinstance(other, Settings)):
            return False
        return self.serialize() == other.serialize()  # TODO: Create proper __eq__ methods for fields and questions

    def add(
        self, property: Union[Field, VectorField, MetadataType, QuestionType], override: bool = True
    ) -> Union[Field, VectorField, MetadataType, QuestionType]:
        """
        Add a property to the settings

        Args:
            property: The property to add
            override: If True, override the existing property with the same name. Otherwise, raise an error.  Defaults to True.

        Returns:
            The added property

        """
        # review all settings properties and remove any existing property with the same name
        for attributes in [self.fields, self.questions, self.vectors, self.metadata]:
            for prop in attributes:
                if prop.name == property.name:
                    message = f"Property with name {property.name!r} already exists in settings as {prop.__class__.__name__!r}"
                    if override:
                        warnings.warn(message + ". Overriding the existing property.")
                        attributes.remove(prop)
                    else:
                        raise SettingsError(message)

        if isinstance(property, FieldBase):
            self.fields.add(property)
        elif isinstance(property, QuestionBase):
            self.questions.add(property)
        elif isinstance(property, VectorField):
            self.vectors.add(property)
        elif isinstance(property, MetadataPropertyBase):
            self.metadata.add(property)
        else:
            raise ValueError(f"Unsupported property type: {type(property).__name__}")
        return property

    #####################
    #  Repr Methods     #
    #####################

    def __repr__(self) -> str:
        return (
            f"Settings(guidelines={self.guidelines}, allow_extra_metadata={self.allow_extra_metadata}, "
            f"distribution={self.distribution}, "
            f"fields={self.fields}, questions={self.questions}, vectors={self.vectors}, metadata={self.metadata})"
        )

    #####################
    #  Private methods  #
    #####################

    @classmethod
    def _from_dict(cls, settings_dict: dict) -> "Settings":
        fields = settings_dict.get("fields", [])
        vectors = settings_dict.get("vectors", [])
        metadata = settings_dict.get("metadata", [])
        guidelines = settings_dict.get("guidelines")
        distribution = settings_dict.get("distribution")
        allow_extra_metadata = settings_dict.get("allow_extra_metadata")
        mapping = settings_dict.get("mapping")

        questions = [_question_from_dict(question) for question in settings_dict.get("questions", [])]
        fields = [_field_from_dict(field) for field in fields]
        vectors = [VectorField.from_dict(vector) for vector in vectors]
        metadata = [MetadataField.from_dict(metadata) for metadata in metadata]

        if distribution:
            distribution = TaskDistribution.from_dict(distribution)

        if mapping:
            mapping = cls._validate_mapping(mapping)

        return cls(
            questions=questions,
            fields=fields,
            vectors=vectors,
            metadata=metadata,
            guidelines=guidelines,
            allow_extra_metadata=allow_extra_metadata,
            distribution=distribution,
            mapping=mapping,
        )

    def _copy(self) -> "Settings":
        instance = self.__class__._from_dict(self.serialize())
        return instance

    def _fetch_fields(self) -> List[Field]:
        models = self._client.api.fields.list(dataset_id=self._dataset.id)
        return [_field_from_model(model) for model in models]

    def _fetch_questions(self) -> List[QuestionType]:
        models = self._client.api.questions.list(dataset_id=self._dataset.id)
        return [question_from_model(model) for model in models]

    def _fetch_vectors(self) -> List[VectorField]:
        models = self.dataset._client.api.vectors.list(self.dataset.id)
        return [VectorField.from_model(model) for model in models]

    def _fetch_metadata(self) -> List[MetadataType]:
        models = self._client.api.metadata.list(dataset_id=self._dataset.id)
        return [MetadataField.from_model(model) for model in models]

    def __fetch_dataset_related_attributes(self):
        # This flow may be a bit weird, but it's the only way to update the dataset related attributes
        # Everything is point that we should have several settings-related endpoints in the API to handle this.
        # POST /api/v1/datasets/{dataset_id}/settings
        # {
        #   "guidelines": ....,
        #   "allow_extra_metadata": ....,
        # }
        # But this is not implemented yet, so we need to update the dataset model directly
        dataset_model = self._client.api.datasets.get(self._dataset.id)

        self.guidelines = dataset_model.guidelines
        self.allow_extra_metadata = dataset_model.allow_extra_metadata

        if dataset_model.distribution:
            self.distribution = TaskDistribution.from_model(dataset_model.distribution)

    def _update_dataset_related_attributes(self):
        # This flow may be a bit weird, but it's the only way to update the dataset related attributes
        # Everything is point that we should have several settings-related endpoints in the API to handle this.
        # POST /api/v1/datasets/{dataset_id}/settings
        # {
        #   "guidelines": ....,
        #   "allow_extra_metadata": ....,
        # }
        # But this is not implemented yet, so we need to update the dataset model directly
        dataset_model = DatasetModel(
            id=self._dataset.id,
            name=self._dataset.name,
            guidelines=self.guidelines,
            allow_extra_metadata=self.allow_extra_metadata,
            distribution=self.distribution._api_model(),
        )
        self._client.api.datasets.update(dataset_model)

    def _validate_empty_settings(self):
        if not all([self.fields, self.questions]):
            message = "Fields and questions are required"
            raise SettingsError(message=message)

    def _validate_duplicate_names(self) -> None:
        dataset_properties_by_name = {}

        for properties in [self.fields, self.questions, self.vectors, self.metadata]:
            for property in properties:
                if property.name in dataset_properties_by_name:
                    raise SettingsError(
                        f"names of dataset settings must be unique, "
                        f"but the name {property.name!r} is used by {type(property).__name__!r} and {type(dataset_properties_by_name[property.name]).__name__!r} "
                    )
                dataset_properties_by_name[property.name] = property

    @classmethod
    def _validate_mapping(cls, mapping: Dict[str, Union[str, Sequence[str]]]) -> dict:
        validate_mapping = {}
        for key, value in mapping.items():
            if isinstance(value, str):
                validate_mapping[key] = value
            elif isinstance(value, list) or isinstance(value, tuple):
                validate_mapping[key] = tuple(value)
            else:
                raise SettingsError(f"Invalid mapping value for key {key!r}: {value}")

        return validate_mapping

    def __process_guidelines(self, guidelines):
        if guidelines is None:
            return guidelines

        if not isinstance(guidelines, str):
            raise SettingsError("Guidelines must be a string or a path to a file")

        if os.path.exists(guidelines):
            with open(guidelines, "r") as file:
                return file.read()

        return guidelines

__init__(fields=None, questions=None, vectors=None, metadata=None, guidelines=None, allow_extra_metadata=False, distribution=None, mapping=None, _dataset=None)

Parameters:

Name Type Description Default
fields List[Field]

A list of Field objects that represent the fields in the Dataset.

None
questions List[Union[LabelQuestion, MultiLabelQuestion, RankingQuestion, TextQuestion, RatingQuestion]]

A list of Question objects that represent the questions in the Dataset.

None
vectors List[VectorField]

A list of VectorField objects that represent the vectors in the Dataset.

None
metadata List[MetadataField]

A list of MetadataField objects that represent the metadata in the Dataset.

None
guidelines str

A string containing the guidelines for the Dataset.

None
allow_extra_metadata bool

A boolean that determines whether or not extra metadata is allowed in the Dataset. Defaults to False.

False
distribution TaskDistribution

The annotation task distribution configuration. Default to DEFAULT_TASK_DISTRIBUTION

None
mapping Dict[str, Union[str, Sequence[str]]]

A dictionary that maps incoming data names to Argilla dataset attributes in DatasetRecords.

None
Source code in src/argilla/settings/_resource.py
def __init__(
    self,
    fields: Optional[List[Field]] = None,
    questions: Optional[List[QuestionType]] = None,
    vectors: Optional[List[VectorField]] = None,
    metadata: Optional[List[MetadataType]] = None,
    guidelines: Optional[str] = None,
    allow_extra_metadata: bool = False,
    distribution: Optional[TaskDistribution] = None,
    mapping: Optional[Dict[str, Union[str, Sequence[str]]]] = None,
    _dataset: Optional["Dataset"] = None,
) -> None:
    """
    Args:
        fields (List[Field]): A list of Field objects that represent the fields in the Dataset.
        questions (List[Union[LabelQuestion, MultiLabelQuestion, RankingQuestion, TextQuestion, RatingQuestion]]):
            A list of Question objects that represent the questions in the Dataset.
        vectors (List[VectorField]): A list of VectorField objects that represent the vectors in the Dataset.
        metadata (List[MetadataField]): A list of MetadataField objects that represent the metadata in the Dataset.
        guidelines (str): A string containing the guidelines for the Dataset.
        allow_extra_metadata (bool): A boolean that determines whether or not extra metadata is allowed in the
            Dataset. Defaults to False.
        distribution (TaskDistribution): The annotation task distribution configuration.
            Default to DEFAULT_TASK_DISTRIBUTION
        mapping (Dict[str, Union[str, Sequence[str]]]): A dictionary that maps incoming data names to Argilla dataset attributes in DatasetRecords.
    """
    super().__init__(client=_dataset._client if _dataset else None)

    self._dataset = _dataset
    self._distribution = distribution or TaskDistribution.default()
    self._mapping = mapping
    self.__guidelines = self.__process_guidelines(guidelines)
    self.__allow_extra_metadata = allow_extra_metadata

    self.__questions = SettingsProperties(self, questions)
    self.__fields = SettingsProperties(self, fields)
    self.__vectors = SettingsProperties(self, vectors)
    self.__metadata = SettingsProperties(self, metadata)

to_json(path)

Save the settings to a file on disk

Parameters:

Name Type Description Default
path str

The path to save the settings to

required
Source code in src/argilla/settings/_resource.py
def to_json(self, path: Union[Path, str]) -> None:
    """Save the settings to a file on disk

    Parameters:
        path (str): The path to save the settings to
    """
    if not isinstance(path, Path):
        path = Path(path)
    if path.exists():
        raise FileExistsError(f"File {path} already exists")
    with open(path, "w") as file:
        json.dump(self.serialize(), file)

from_json(path) classmethod

Load the settings from a file on disk

Source code in src/argilla/settings/_resource.py
@classmethod
def from_json(cls, path: Union[Path, str]) -> "Settings":
    """Load the settings from a file on disk"""

    with open(path, "r") as file:
        settings_dict = json.load(file)
        return cls._from_dict(settings_dict)

from_hub(repo_id, subset=None, feature_mapping=None, **kwargs) classmethod

Load the settings from the Hub

Parameters:

Name Type Description Default
repo_id str

The ID of the repository to load the settings from on the Hub.

required
subset Optional[str]

The subset of the repository to load the settings from.

None
feature_mapping Dict[str, Literal['question', 'field', 'metadata']]

A dictionary that maps incoming column names to Argilla attributes.

None
Source code in src/argilla/settings/_resource.py
@classmethod
def from_hub(
    cls,
    repo_id: str,
    subset: Optional[str] = None,
    feature_mapping: Optional[Dict[str, Literal["question", "field", "metadata"]]] = None,
    **kwargs,
) -> "Settings":
    """Load the settings from the Hub

    Parameters:
        repo_id (str): The ID of the repository to load the settings from on the Hub.
        subset (Optional[str]): The subset of the repository to load the settings from.
        feature_mapping (Dict[str, Literal["question", "field", "metadata"]]): A dictionary that maps incoming column names to Argilla attributes.
    """

    settings = build_settings_from_repo_id(repo_id=repo_id, feature_mapping=feature_mapping, subset=subset)
    return settings

add(property, override=True)

Add a property to the settings

Parameters:

Name Type Description Default
property Union[Field, VectorField, MetadataType, QuestionType]

The property to add

required
override bool

If True, override the existing property with the same name. Otherwise, raise an error. Defaults to True.

True

Returns:

Type Description
Union[Field, VectorField, MetadataType, QuestionType]

The added property

Source code in src/argilla/settings/_resource.py
def add(
    self, property: Union[Field, VectorField, MetadataType, QuestionType], override: bool = True
) -> Union[Field, VectorField, MetadataType, QuestionType]:
    """
    Add a property to the settings

    Args:
        property: The property to add
        override: If True, override the existing property with the same name. Otherwise, raise an error.  Defaults to True.

    Returns:
        The added property

    """
    # review all settings properties and remove any existing property with the same name
    for attributes in [self.fields, self.questions, self.vectors, self.metadata]:
        for prop in attributes:
            if prop.name == property.name:
                message = f"Property with name {property.name!r} already exists in settings as {prop.__class__.__name__!r}"
                if override:
                    warnings.warn(message + ". Overriding the existing property.")
                    attributes.remove(prop)
                else:
                    raise SettingsError(message)

    if isinstance(property, FieldBase):
        self.fields.add(property)
    elif isinstance(property, QuestionBase):
        self.questions.add(property)
    elif isinstance(property, VectorField):
        self.vectors.add(property)
    elif isinstance(property, MetadataPropertyBase):
        self.metadata.add(property)
    else:
        raise ValueError(f"Unsupported property type: {type(property).__name__}")
    return property