medcat.utils.regression.targeting ================================= .. py:module:: medcat.utils.regression.targeting Attributes ---------- .. autoapisummary:: medcat.utils.regression.targeting.logger Exceptions ---------- .. autoapisummary:: medcat.utils.regression.targeting.ProblematicOptionSetException Classes ------- .. autoapisummary:: medcat.utils.regression.targeting.CDB medcat.utils.regression.targeting.CUIInfo medcat.utils.regression.targeting.NameInfo medcat.utils.regression.targeting.TranslationLayer medcat.utils.regression.targeting.TargetPlaceholder medcat.utils.regression.targeting.PhraseChanger medcat.utils.regression.targeting.TargetedPhraseChanger medcat.utils.regression.targeting.FinalTarget medcat.utils.regression.targeting.OptionSet Module Contents --------------- .. py:class:: CDB(config) Bases: :py:obj:`medcat.storage.serialisables.AbstractSerialisable` The abstract serialisable base class. This defines some common defaults. .. py:method:: __init__(config) .. py:attribute:: config .. py:attribute:: cui2info :type: dict[str, medcat.cdb.concepts.CUIInfo] .. py:attribute:: name2info :type: dict[str, medcat.cdb.concepts.NameInfo] .. py:attribute:: type_id2info :type: dict[str, medcat.cdb.concepts.TypeInfo] .. py:attribute:: token_counts :type: dict[str, int] .. py:attribute:: addl_info :type: dict[str, Any] .. py:attribute:: _subnames :type: set[str] .. py:attribute:: is_dirty :value: False .. py:attribute:: has_changed_names :value: False .. py:method:: get_init_attrs() :classmethod: .. py:method:: _reset_subnames() .. py:method:: has_subname(name) Whether the CDB has the specified subname. :param name: The subname to check. :type name: str :Returns: **bool** -- Whether the subname is present in this CDB. .. py:method:: get_name(cui) Returns preferred name if it exists, otherwise it will return the longest name assigned to the concept. :param cui: Concept ID or unique identifier in this database. :type cui: str :Returns: **str** -- The name of the concept. .. py:method:: weighted_average_function(step) Get the weighted average for steop. :param step: The steop. :type step: int :Returns: **float** -- The weighted average. .. py:method:: add_types(types) Add type info to CDB. :param types: The raw type info. :type types: Iterable[tuple[str, str]] .. py:method:: add_names(cui, names, name_status = ST.AUTOMATIC, full_build = False) Adds a name to an existing concept. :param cui: Concept ID or unique identifier in this database, all concepts that have the same CUI will be merged internally. :type cui: str :param names: Names for this concept, or the value that if found in free text can be linked to this concept. Names is an dict like: `{name: {'tokens': tokens, 'snames': snames, 'raw_name': raw_name}, ...}` Names should be generated by helper function 'medcat.preprocessing.cleaners.prepare_name' :type names: dict[str, NameDescriptor] :param name_status: One of `P`, `N`, `A`. Defaults to 'A'. :type name_status: str :param full_build: If True the dictionary self.addl_info will also be populated, contains a lot of extra information about concepts, but can be very memory consuming. This is not necessary for normal functioning of MedCAT (Default value `False`). :type full_build: bool .. py:method:: _add_concept_names(cui, names, name_status) .. py:method:: _add_full_build(cui, names, ontologies, description, type_ids) .. py:method:: _add_concept(cui, names, ontologies, name_status, type_ids, description, full_build = False) Add a concept to internal Concept Database (CDB). Depending on what you are providing this will add a large number of properties for each concept. :param cui: Concept ID or unique identifier in this database, all concepts that have the same CUI will be merged internally. :type cui: str :param names: Names for this concept, or the value that if found in free text can be linked to this concept. Names is a dict like: `{name: {'tokens': tokens, 'snames': snames, 'raw_name': raw_name}, ...}` Names should be generated by helper function 'medcat.preprocessing.cleaners.prepare_name' :type names: dict[str, NameDescriptor] :param ontologies: ontologies in which the concept exists (e.g. SNOMEDCT, HPO) :type ontologies: set[str] :param name_status: One of `P`, `N`, `A` :type name_status: str :param type_ids: Semantic type identifier (have a look at TUIs in UMLS or SNOMED-CT) :type type_ids: set[str] :param description: Description of this concept. :type description: str :param full_build: If True the dictionary self.addl_info will also be populated, contains a lot of extra information about concepts, but can be very memory consuming. This is not necessary for normal functioning of MedCAT (Default Value `False`). :type full_build: bool .. py:method:: reset_training() Will remove all training efforts - in other words all embeddings that are learnt for concepts in the current CDB. Please note that this does not remove synonyms (names) that were potentially added during supervised/online learning. .. py:method:: filter_by_cui(cuis_to_keep) Subset the core CDB fields (dictionaries/maps). Note that this will potenitally keep a bit more CUIs then in cuis_to_keep. It will first find all names that link to the cuis_to_keep and then find all CUIs that link to those names and keep all of them. This also will not remove any data from cdb.addl_info - as this field can contain data of unknown structure. :param cuis_to_keep: CUIs that will be kept, the rest will be removed (not completely, look above). :type cuis_to_keep: Collection[str] :raises Exception: If no snames and subsetting is not possible. .. py:method:: remove_cui(cui) This function takes a CUI and removes it the CDB. It also removes the CUI from name specific per_cui_status maps as well as well as removes all the names that do not correspond to any CUIs after the removal of this one. :param cui: The CUI to remove. :type cui: str .. py:method:: _remove_names(cui, names) Remove names from an existing concept - effect is this name will never again be used to link to this concept. This will only remove the name from the linker (namely name2cuis and name2cuis2status), the name will still be present everywhere else. Why? Because it is bothersome to remove it from everywhere, but could also be useful to keep the removed names in e.g. cui2names. :param cui: Concept ID or unique identifier in this database. :type cui: str :param names: Names to be removed (e.g list, set, or even a dict (in which case keys will be used)). :type names: Iterable[str] .. py:method:: __eq__(other) .. py:method:: get_cui2count_train() .. py:method:: get_name2count_train() .. py:method:: get_hash() .. py:method:: get_basic_info() .. py:method:: save(save_path, serialiser = AvailableSerialisers.dill, overwrite = False) Save CDB at path. :param save_path: The path to save at. :type save_path: str :param serialiser: The serialiser. Defaults to AvailableSerialisers.dill. :type serialiser: Union[ str, AvailableSerialisers], optional :param overwrite: Whether to allow overwriting existing files. Defaults to False. :type overwrite: bool, optional .. py:method:: load(path) :classmethod: .. py:method:: get_strategy() .. py:method:: ignore_attrs() :classmethod: .. py:method:: include_properties() :classmethod: .. py:class:: CUIInfo Bases: :py:obj:`TypedDict` dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2) .. py:attribute:: cui :type: str .. py:attribute:: preferred_name :type: str .. py:attribute:: names :type: set[str] .. py:attribute:: subnames :type: set[str] .. py:attribute:: type_ids :type: set[str] .. py:attribute:: description :type: Optional[str] .. py:attribute:: original_names :type: Optional[set[str]] .. py:attribute:: tags :type: Optional[list[str]] .. py:attribute:: group :type: Optional[str] .. py:attribute:: in_other_ontology :type: Optional[set[str]] .. py:attribute:: count_train :type: int .. py:attribute:: context_vectors :type: Optional[dict[str, numpy.ndarray]] .. py:attribute:: average_confidence :type: float .. py:method:: __contains__() True if the dictionary has the specified key, else False. .. py:method:: __delattr__() Implement delattr(self, name). .. py:method:: __delitem__() Delete self[key]. .. py:method:: __dir__() Default dir() implementation. .. py:method:: __eq__() Return self==value. .. py:method:: __format__() Default object formatter. .. py:method:: __ge__() Return self>=value. .. py:method:: __getattribute__() Return getattr(self, name). .. py:method:: __getitem__() x.__getitem__(y) <==> x[y] .. py:method:: __gt__() Return self>value. .. py:method:: __init__() Initialize self. See help(type(self)) for accurate signature. .. py:method:: __ior__() Return self|=value. .. py:method:: __iter__() Implement iter(self). .. py:method:: __le__() Return self<=value. .. py:method:: __len__() Return len(self). .. py:method:: __lt__() Return self size of D in memory, in bytes .. py:method:: __str__() Return str(self). .. py:method:: __subclasshook__() Abstract classes can override this to customize issubclass(). This is invoked early on by abc.ABCMeta.__subclasscheck__(). It should return True, False or NotImplemented. If it returns NotImplemented, the normal algorithm is used. Otherwise, it overrides the normal algorithm (and the outcome is cached). .. py:method:: clear() D.clear() -> None. Remove all items from D. .. py:method:: copy() D.copy() -> a shallow copy of D .. py:method:: get() Return the value for key if key is in the dictionary, else default. .. py:method:: items() D.items() -> a set-like object providing a view on D's items .. py:method:: keys() D.keys() -> a set-like object providing a view on D's keys .. py:method:: pop() D.pop(k[,d]) -> v, remove specified key and return the corresponding value. If the key is not found, return the default if given; otherwise, raise a KeyError. .. py:method:: popitem() Remove and return a (key, value) pair as a 2-tuple. Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict is empty. .. py:method:: setdefault() Insert key with a value of default if key is not in the dictionary. Return the value for key if key is in the dictionary, else default. .. py:method:: update() D.update([E, ]**F) -> None. Update D from dict/iterable E and F. If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k] .. py:method:: values() D.values() -> an object providing a view on D's values .. py:class:: NameInfo Bases: :py:obj:`TypedDict` dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2) .. py:attribute:: name :type: str .. py:attribute:: per_cui_status :type: dict[str, str] .. py:attribute:: is_upper :type: bool .. py:attribute:: count_train :type: int .. py:method:: __contains__() True if the dictionary has the specified key, else False. .. py:method:: __delattr__() Implement delattr(self, name). .. py:method:: __delitem__() Delete self[key]. .. py:method:: __dir__() Default dir() implementation. .. py:method:: __eq__() Return self==value. .. py:method:: __format__() Default object formatter. .. py:method:: __ge__() Return self>=value. .. py:method:: __getattribute__() Return getattr(self, name). .. py:method:: __getitem__() x.__getitem__(y) <==> x[y] .. py:method:: __gt__() Return self>value. .. py:method:: __init__() Initialize self. See help(type(self)) for accurate signature. .. py:method:: __ior__() Return self|=value. .. py:method:: __iter__() Implement iter(self). .. py:method:: __le__() Return self<=value. .. py:method:: __len__() Return len(self). .. py:method:: __lt__() Return self size of D in memory, in bytes .. py:method:: __str__() Return str(self). .. py:method:: __subclasshook__() Abstract classes can override this to customize issubclass(). This is invoked early on by abc.ABCMeta.__subclasscheck__(). It should return True, False or NotImplemented. If it returns NotImplemented, the normal algorithm is used. Otherwise, it overrides the normal algorithm (and the outcome is cached). .. py:method:: clear() D.clear() -> None. Remove all items from D. .. py:method:: copy() D.copy() -> a shallow copy of D .. py:method:: get() Return the value for key if key is in the dictionary, else default. .. py:method:: items() D.items() -> a set-like object providing a view on D's items .. py:method:: keys() D.keys() -> a set-like object providing a view on D's keys .. py:method:: pop() D.pop(k[,d]) -> v, remove specified key and return the corresponding value. If the key is not found, return the default if given; otherwise, raise a KeyError. .. py:method:: popitem() Remove and return a (key, value) pair as a 2-tuple. Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict is empty. .. py:method:: setdefault() Insert key with a value of default if key is not in the dictionary. Return the value for key if key is in the dictionary, else default. .. py:method:: update() D.update([E, ]**F) -> None. Update D from dict/iterable E and F. If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k] .. py:method:: values() D.values() -> an object providing a view on D's values .. py:data:: logger .. py:class:: TranslationLayer(cui2info, name2info, cui2children, separator, whitespace = ' ') The translation layer for translating: - CUIs to names - names to CUIs - type_ids to CUIs - CUIs to chil CUIs The idea is to decouple these translations from the CDB instance in case something changes there. :param cui2info: The map from CUI to names :type cui2info: dict[str, CUIInfo] :param name2info: The map from name to CUIs :type name2info: dict[str, NameInfo] :param cui2type_ids: The map from CUI to type_ids :type cui2type_ids: dict[str, set[str]] :param cui2children: The map from CUI to child CUIs :type cui2children: dict[str, set[str]] .. py:method:: __init__(cui2info, name2info, cui2children, separator, whitespace = ' ') .. py:attribute:: cui2info .. py:attribute:: name2info .. py:attribute:: separator .. py:attribute:: whitespace :value: ' ' .. py:attribute:: type_id2cuis :type: dict[str, set[str]] .. py:attribute:: cui2children .. py:method:: get_names_of(cui, only_prefnames) Get the preprocessed names of a CUI. This method preporcesses the names by replacing the separator (generally `~`) with the appropriate whitespace (` `). If the concept is not in the underlying CDB, an empty list is returned. :param cui: The concept in question. :type cui: str :param only_prefnames: Whether to only return a preferred name. :type only_prefnames: bool :Returns: **list[str]** -- The list of names. .. py:method:: get_preferred_name(cui) Get the preferred name of a concept. If no preferred name is found, the random 'first' name is selected. :param cui: The concept ID. :type cui: str :Returns: **str** -- The preferred name. .. py:method:: get_first_name(cui) Get the preprocessed (potentially) arbitrarily first name of the given concept. If the concept does not exist, the CUI itself is returned. PS: The "first" name may not be consistent across runs since it relies on set order. :param cui: The concept ID. :type cui: str :Returns: **str** -- The first name. .. py:method:: get_direct_children(cui) Get the direct children of a concept. This means only the children, but not grandchildren. If the underlying CDB doesn't list children for this CUI, an empty list is returned. :param cui: The concept in question. :type cui: str :Returns: **list[str]** -- The (potentially empty) list of direct children. .. py:method:: get_direct_parents(cui) Get the direct parent(s) of a concept. PS: This method can be quite a CPU heavy one since it relies on running through all the parent-children relationships since the child->parent(s) relationship isn't normally kept track of. :param cui: _description_ :type cui: str :Returns: **list[str]** -- _description_ .. py:method:: get_children_of(found_cuis, cui, depth = 1) Get the children of the specifeid CUI in the listed CUIs (if they exist). :param found_cuis: The list of CUIs to look in :type found_cuis: Iterable[str] :param cui: The target parent CUI :type cui: str :param depth: The depth to carry out the search for :type depth: int :Returns: **list[str]** -- The list of children found .. py:method:: from_CDB(cdb) :classmethod: Construct a TranslationLayer object from a context database (CDB). This translation layer will refer to the same dicts that the CDB refers to. While there is no obvious reason these should be modified, it's something to keep in mind. :param cdb: The CDB :type cdb: CDB :Returns: **TranslationLayer** -- The subsequent TranslationLayer .. py:class:: TargetPlaceholder(/, **data) Bases: :py:obj:`pydantic.BaseModel` A class describing the options for a specific placeholder. .. py:attribute:: placeholder :type: str .. py:attribute:: target_cuis :type: list[str] .. py:attribute:: onlyprefnames :type: bool :value: False .. py:attribute:: model_config :type: ClassVar[pydantic.config.ConfigDict] Configuration for the model, should be a dictionary conforming to [`ConfigDict`][pydantic.config.ConfigDict]. .. py:attribute:: model_fields :type: ClassVar[Dict[str, pydantic.fields.FieldInfo]] Metadata about the fields defined on the model, mapping of field names to [`FieldInfo`][pydantic.fields.FieldInfo] objects. This replaces `Model.__fields__` from Pydantic V1. .. py:attribute:: model_computed_fields :type: ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]] A dictionary of computed field names and their corresponding `ComputedFieldInfo` objects. .. py:attribute:: __class_vars__ :type: ClassVar[set[str]] The names of the class variables defined on the model. .. py:attribute:: __private_attributes__ :type: ClassVar[Dict[str, pydantic.fields.ModelPrivateAttr]] Metadata about the private attributes of the model. .. py:attribute:: __signature__ :type: ClassVar[inspect.Signature] The synthesized `__init__` [`Signature`][inspect.Signature] of the model. .. py:attribute:: __pydantic_complete__ :type: ClassVar[bool] :value: False Whether model building is completed, or if there are still undefined fields. .. py:attribute:: __pydantic_core_schema__ :type: ClassVar[pydantic_core.CoreSchema] The core schema of the model. .. py:attribute:: __pydantic_custom_init__ :type: ClassVar[bool] Whether the model has a custom `__init__` method. .. py:attribute:: __pydantic_decorators__ :type: ClassVar[pydantic._internal._decorators.DecoratorInfos] Metadata containing the decorators defined on the model. This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1. .. py:attribute:: __pydantic_generic_metadata__ :type: ClassVar[pydantic._internal._generics.PydanticGenericMetadata] Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these. .. py:attribute:: __pydantic_parent_namespace__ :type: ClassVar[Dict[str, Any] | None] :value: None Parent namespace of the model, used for automatic rebuilding of models. .. py:attribute:: __pydantic_post_init__ :type: ClassVar[None | Literal['model_post_init']] The name of the post-init method for the model, if defined. .. py:attribute:: __pydantic_root_model__ :type: ClassVar[bool] :value: False Whether the model is a [`RootModel`][pydantic.root_model.RootModel]. .. py:attribute:: __pydantic_serializer__ :type: ClassVar[pydantic_core.SchemaSerializer] The `pydantic-core` `SchemaSerializer` used to dump instances of the model. .. py:attribute:: __pydantic_validator__ :type: ClassVar[pydantic_core.SchemaValidator | pydantic.plugin._schema_validator.PluggableSchemaValidator] The `pydantic-core` `SchemaValidator` used to validate instances of the model. .. py:attribute:: __pydantic_extra__ :type: dict[str, Any] | None A dictionary containing extra values, if [`extra`][pydantic.config.ConfigDict.extra] is set to `'allow'`. .. py:attribute:: __pydantic_fields_set__ :type: set[str] The names of fields explicitly set during instantiation. .. py:attribute:: __pydantic_private__ :type: dict[str, Any] | None Values of private attributes set on the model instance. .. py:attribute:: __slots__ :value: ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__') .. py:method:: __init__(/, **data) 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. .. py:property:: model_extra :type: dict[str, Any] | None Get extra fields set during validation. :Returns: **A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.** .. py:property:: model_fields_set :type: set[str] 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. .. py:method:: model_construct(_fields_set = None, **values) :classmethod: 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. !!! note `model_construct()` generally respects the `model_config.extra` setting on the provided model. That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__` and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored. Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in an error if extra values are passed, but they will be ignored. :param _fields_set: A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the `values` argument will be used. :param values: Trusted or pre-validated data dictionary. :Returns: **A new instance of the `Model` class with validated data.** .. py:method:: model_copy(*, update = None, deep = False) Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy Returns a copy of the model. :param 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. :param deep: Set to `True` to make a deep copy of the model. :Returns: **New model instance.** .. py:method:: model_dump(*, mode = 'python', include = None, exclude = None, context = None, by_alias = False, exclude_unset = False, exclude_defaults = False, exclude_none = False, round_trip = False, warnings = True, serialize_as_any = False) Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump Generate a dictionary representation of the model, optionally specifying which fields to include or exclude. :param mode: The mode in which `to_python` should run. If mode is 'json', the output will only contain JSON serializable types. If mode is 'python', the output may contain non-JSON-serializable Python objects. :param include: A set of fields to include in the output. :param exclude: A set of fields to exclude from the output. :param context: Additional context to pass to the serializer. :param by_alias: Whether to use the field's alias in the dictionary key if defined. :param exclude_unset: Whether to exclude fields that have not been explicitly set. :param exclude_defaults: Whether to exclude fields that are set to their default value. :param exclude_none: Whether to exclude fields that have a value of `None`. :param round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T]. :param warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError]. :param serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. :Returns: **A dictionary representation of the model.** .. py:method:: model_dump_json(*, indent = None, include = None, exclude = None, context = None, by_alias = False, exclude_unset = False, exclude_defaults = False, exclude_none = False, round_trip = False, warnings = True, serialize_as_any = False) Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json Generates a JSON representation of the model using Pydantic's `to_json` method. :param indent: Indentation to use in the JSON output. If None is passed, the output will be compact. :param include: Field(s) to include in the JSON output. :param exclude: Field(s) to exclude from the JSON output. :param context: Additional context to pass to the serializer. :param by_alias: Whether to serialize using field aliases. :param exclude_unset: Whether to exclude fields that have not been explicitly set. :param exclude_defaults: Whether to exclude fields that are set to their default value. :param exclude_none: Whether to exclude fields that have a value of `None`. :param round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T]. :param warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError]. :param serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. :Returns: **A JSON string representation of the model.** .. py:method:: model_json_schema(by_alias = True, ref_template = DEFAULT_REF_TEMPLATE, schema_generator = GenerateJsonSchema, mode = 'validation') :classmethod: Generates a JSON schema for a model class. :param by_alias: Whether to use attribute aliases or not. :param ref_template: The reference template. :param schema_generator: To override the logic used to generate the JSON schema, as a subclass of `GenerateJsonSchema` with your desired modifications :param mode: The mode in which to generate the schema. :Returns: **The JSON schema for the given model class.** .. py:method:: model_parametrized_name(params) :classmethod: Compute the class name for parametrizations of generic classes. This method can be overridden to achieve a custom naming scheme for generic BaseModels. :param 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. .. py:method:: model_post_init(__context) 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. .. py:method:: model_rebuild(*, force = False, raise_errors = True, _parent_namespace_depth = 2, _types_namespace = None) :classmethod: 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. :param force: Whether to force the rebuilding of the model schema, defaults to `False`. :param raise_errors: Whether to raise errors, defaults to `True`. :param _parent_namespace_depth: The depth level of the parent namespace, defaults to 2. :param _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`.** .. py:method:: model_validate(obj, *, strict = None, from_attributes = None, context = None) :classmethod: Validate a pydantic model instance. :param obj: The object to validate. :param strict: Whether to enforce types strictly. :param from_attributes: Whether to extract data from object attributes. :param context: Additional context to pass to the validator. :raises ValidationError: If the object could not be validated. :Returns: **The validated model instance.** .. py:method:: model_validate_json(json_data, *, strict = None, context = None) :classmethod: Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing Validate the given JSON data against the Pydantic model. :param json_data: The JSON data to validate. :param strict: Whether to enforce types strictly. :param context: Extra variables to pass to the validator. :Returns: **The validated Pydantic model.** :raises ValidationError: If `json_data` is not a JSON string or the object could not be validated. .. py:method:: model_validate_strings(obj, *, strict = None, context = None) :classmethod: Validate the given object with string data against the Pydantic model. :param obj: The object containing string data to validate. :param strict: Whether to enforce types strictly. :param context: Extra variables to pass to the validator. :Returns: **The validated Pydantic model.** .. py:method:: __get_pydantic_core_schema__(source, handler, /) :classmethod: Hook into generating the model's CoreSchema. :param source: The class we are generating a schema for. This will generally be the same as the `cls` argument if this is a classmethod. :param handler: A callable that calls into Pydantic's internal CoreSchema generation logic. :Returns: **A `pydantic-core` `CoreSchema`.** .. py:method:: __get_pydantic_json_schema__(core_schema, handler, /) :classmethod: Hook into generating the model's JSON schema. :param core_schema: A `pydantic-core` CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`), or just call the handler with the original schema. :param handler: Call into Pydantic's internal JSON schema generation. This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema generation fails. Since this gets called by `BaseModel.model_json_schema` you can override the `schema_generator` argument to that function to change JSON schema generation globally for a type. :Returns: **A JSON schema, as a Python object.** .. py:method:: __pydantic_init_subclass__(**kwargs) :classmethod: This is intended to behave just like `__init_subclass__`, but is called by `ModelMetaclass` only after the class is actually fully initialized. In particular, attributes like `model_fields` will be present when this is called. This is necessary because `__init_subclass__` will always be called by `type.__new__`, and it would require a prohibitively large refactor to the `ModelMetaclass` to ensure that `type.__new__` was called in such a manner that the class would already be sufficiently initialized. This will receive the same `kwargs` that would be passed to the standard `__init_subclass__`, namely, any kwargs passed to the class definition that aren't used internally by pydantic. :param \*\*kwargs: Any keyword arguments passed to the class definition that aren't used internally by pydantic. .. py:method:: __class_getitem__(typevar_values) :classmethod: .. py:method:: __copy__() Returns a shallow copy of the model. .. py:method:: __deepcopy__(memo = None) Returns a deep copy of the model. .. py:method:: __getattr__(item) .. py:method:: _check_frozen(name, value) .. py:method:: __getstate__() .. py:method:: __setstate__(state) .. py:method:: __eq__(other) .. py:method:: __init_subclass__(**kwargs) :classmethod: This signature is included purely to help type-checkers check arguments to class declaration, which provides a way to conveniently set model_config key/value pairs. ```py from pydantic import BaseModel class MyModel(BaseModel, extra='allow'): ... ``` However, this may be deceiving, since the _actual_ calls to `__init_subclass__` will not receive any of the config arguments, and will only receive any keyword arguments passed during class initialization that are _not_ expected keys in ConfigDict. (This is due to the way `ModelMetaclass.__new__` works.) :param \*\*kwargs: Keyword arguments passed to the class definition, which set model_config .. note:: You may want to override `__pydantic_init_subclass__` instead, which behaves similarly but is called *after* the class is fully initialized. .. py:method:: __iter__() So `dict(model)` works. .. py:method:: __repr__() .. py:method:: __repr_args__() .. py:attribute:: __repr_name__ .. py:attribute:: __repr_str__ .. py:attribute:: __pretty__ .. py:attribute:: __rich_repr__ .. py:method:: __str__() .. py:property:: __fields__ :type: dict[str, pydantic.fields.FieldInfo] .. py:property:: __fields_set__ :type: set[str] .. py:method:: dict(*, include = None, exclude = None, by_alias = False, exclude_unset = False, exclude_defaults = False, exclude_none = False) .. py:method:: json(*, include = None, exclude = None, by_alias = False, exclude_unset = False, exclude_defaults = False, exclude_none = False, encoder = PydanticUndefined, models_as_dict = PydanticUndefined, **dumps_kwargs) .. py:method:: parse_obj(obj) :classmethod: .. py:method:: parse_raw(b, *, content_type = None, encoding = 'utf8', proto = None, allow_pickle = False) :classmethod: .. py:method:: parse_file(path, *, content_type = None, encoding = 'utf8', proto = None, allow_pickle = False) :classmethod: .. py:method:: from_orm(obj) :classmethod: .. py:method:: construct(_fields_set = None, **values) :classmethod: .. py:method:: copy(*, include = None, exclude = None, update = None, deep = False) 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) ``` :param include: Optional set or mapping specifying which fields to include in the copied model. :param exclude: Optional set or mapping specifying which fields to exclude in the copied model. :param update: Optional dictionary of field-value pairs to override field values in the copied model. :param 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.** .. py:method:: schema(by_alias = True, ref_template = DEFAULT_REF_TEMPLATE) :classmethod: .. py:method:: schema_json(*, by_alias = True, ref_template = DEFAULT_REF_TEMPLATE, **dumps_kwargs) :classmethod: .. py:method:: validate(value) :classmethod: .. py:method:: update_forward_refs(**localns) :classmethod: .. py:method:: _iter(*args, **kwargs) .. py:method:: _copy_and_set_values(*args, **kwargs) .. py:method:: _get_value(*args, **kwargs) :classmethod: .. py:method:: _calculate_keys(*args, **kwargs) .. py:class:: PhraseChanger(/, **data) Bases: :py:obj:`pydantic.BaseModel` The phrase changer. This is class used as a preprocessor for phrases with multiple placeholders. It allows swapping in the rest of the placeholders while leaving in the one that's being tested for. .. py:attribute:: preprocess_placeholders :type: list[tuple[str, str]] .. py:method:: __call__(phrase) .. py:method:: empty() :classmethod: Gets the empty phrase changer. That is a phrase changer that makes no changes to the phrase. :Returns: **PhraseChanger** -- The empty phrase changer. .. py:attribute:: model_config :type: ClassVar[pydantic.config.ConfigDict] Configuration for the model, should be a dictionary conforming to [`ConfigDict`][pydantic.config.ConfigDict]. .. py:attribute:: model_fields :type: ClassVar[Dict[str, pydantic.fields.FieldInfo]] Metadata about the fields defined on the model, mapping of field names to [`FieldInfo`][pydantic.fields.FieldInfo] objects. This replaces `Model.__fields__` from Pydantic V1. .. py:attribute:: model_computed_fields :type: ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]] A dictionary of computed field names and their corresponding `ComputedFieldInfo` objects. .. py:attribute:: __class_vars__ :type: ClassVar[set[str]] The names of the class variables defined on the model. .. py:attribute:: __private_attributes__ :type: ClassVar[Dict[str, pydantic.fields.ModelPrivateAttr]] Metadata about the private attributes of the model. .. py:attribute:: __signature__ :type: ClassVar[inspect.Signature] The synthesized `__init__` [`Signature`][inspect.Signature] of the model. .. py:attribute:: __pydantic_complete__ :type: ClassVar[bool] :value: False Whether model building is completed, or if there are still undefined fields. .. py:attribute:: __pydantic_core_schema__ :type: ClassVar[pydantic_core.CoreSchema] The core schema of the model. .. py:attribute:: __pydantic_custom_init__ :type: ClassVar[bool] Whether the model has a custom `__init__` method. .. py:attribute:: __pydantic_decorators__ :type: ClassVar[pydantic._internal._decorators.DecoratorInfos] Metadata containing the decorators defined on the model. This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1. .. py:attribute:: __pydantic_generic_metadata__ :type: ClassVar[pydantic._internal._generics.PydanticGenericMetadata] Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these. .. py:attribute:: __pydantic_parent_namespace__ :type: ClassVar[Dict[str, Any] | None] :value: None Parent namespace of the model, used for automatic rebuilding of models. .. py:attribute:: __pydantic_post_init__ :type: ClassVar[None | Literal['model_post_init']] The name of the post-init method for the model, if defined. .. py:attribute:: __pydantic_root_model__ :type: ClassVar[bool] :value: False Whether the model is a [`RootModel`][pydantic.root_model.RootModel]. .. py:attribute:: __pydantic_serializer__ :type: ClassVar[pydantic_core.SchemaSerializer] The `pydantic-core` `SchemaSerializer` used to dump instances of the model. .. py:attribute:: __pydantic_validator__ :type: ClassVar[pydantic_core.SchemaValidator | pydantic.plugin._schema_validator.PluggableSchemaValidator] The `pydantic-core` `SchemaValidator` used to validate instances of the model. .. py:attribute:: __pydantic_extra__ :type: dict[str, Any] | None A dictionary containing extra values, if [`extra`][pydantic.config.ConfigDict.extra] is set to `'allow'`. .. py:attribute:: __pydantic_fields_set__ :type: set[str] The names of fields explicitly set during instantiation. .. py:attribute:: __pydantic_private__ :type: dict[str, Any] | None Values of private attributes set on the model instance. .. py:attribute:: __slots__ :value: ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__') .. py:method:: __init__(/, **data) 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. .. py:property:: model_extra :type: dict[str, Any] | None Get extra fields set during validation. :Returns: **A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.** .. py:property:: model_fields_set :type: set[str] 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. .. py:method:: model_construct(_fields_set = None, **values) :classmethod: 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. !!! note `model_construct()` generally respects the `model_config.extra` setting on the provided model. That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__` and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored. Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in an error if extra values are passed, but they will be ignored. :param _fields_set: A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the `values` argument will be used. :param values: Trusted or pre-validated data dictionary. :Returns: **A new instance of the `Model` class with validated data.** .. py:method:: model_copy(*, update = None, deep = False) Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy Returns a copy of the model. :param 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. :param deep: Set to `True` to make a deep copy of the model. :Returns: **New model instance.** .. py:method:: model_dump(*, mode = 'python', include = None, exclude = None, context = None, by_alias = False, exclude_unset = False, exclude_defaults = False, exclude_none = False, round_trip = False, warnings = True, serialize_as_any = False) Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump Generate a dictionary representation of the model, optionally specifying which fields to include or exclude. :param mode: The mode in which `to_python` should run. If mode is 'json', the output will only contain JSON serializable types. If mode is 'python', the output may contain non-JSON-serializable Python objects. :param include: A set of fields to include in the output. :param exclude: A set of fields to exclude from the output. :param context: Additional context to pass to the serializer. :param by_alias: Whether to use the field's alias in the dictionary key if defined. :param exclude_unset: Whether to exclude fields that have not been explicitly set. :param exclude_defaults: Whether to exclude fields that are set to their default value. :param exclude_none: Whether to exclude fields that have a value of `None`. :param round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T]. :param warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError]. :param serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. :Returns: **A dictionary representation of the model.** .. py:method:: model_dump_json(*, indent = None, include = None, exclude = None, context = None, by_alias = False, exclude_unset = False, exclude_defaults = False, exclude_none = False, round_trip = False, warnings = True, serialize_as_any = False) Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json Generates a JSON representation of the model using Pydantic's `to_json` method. :param indent: Indentation to use in the JSON output. If None is passed, the output will be compact. :param include: Field(s) to include in the JSON output. :param exclude: Field(s) to exclude from the JSON output. :param context: Additional context to pass to the serializer. :param by_alias: Whether to serialize using field aliases. :param exclude_unset: Whether to exclude fields that have not been explicitly set. :param exclude_defaults: Whether to exclude fields that are set to their default value. :param exclude_none: Whether to exclude fields that have a value of `None`. :param round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T]. :param warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError]. :param serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. :Returns: **A JSON string representation of the model.** .. py:method:: model_json_schema(by_alias = True, ref_template = DEFAULT_REF_TEMPLATE, schema_generator = GenerateJsonSchema, mode = 'validation') :classmethod: Generates a JSON schema for a model class. :param by_alias: Whether to use attribute aliases or not. :param ref_template: The reference template. :param schema_generator: To override the logic used to generate the JSON schema, as a subclass of `GenerateJsonSchema` with your desired modifications :param mode: The mode in which to generate the schema. :Returns: **The JSON schema for the given model class.** .. py:method:: model_parametrized_name(params) :classmethod: Compute the class name for parametrizations of generic classes. This method can be overridden to achieve a custom naming scheme for generic BaseModels. :param 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. .. py:method:: model_post_init(__context) 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. .. py:method:: model_rebuild(*, force = False, raise_errors = True, _parent_namespace_depth = 2, _types_namespace = None) :classmethod: 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. :param force: Whether to force the rebuilding of the model schema, defaults to `False`. :param raise_errors: Whether to raise errors, defaults to `True`. :param _parent_namespace_depth: The depth level of the parent namespace, defaults to 2. :param _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`.** .. py:method:: model_validate(obj, *, strict = None, from_attributes = None, context = None) :classmethod: Validate a pydantic model instance. :param obj: The object to validate. :param strict: Whether to enforce types strictly. :param from_attributes: Whether to extract data from object attributes. :param context: Additional context to pass to the validator. :raises ValidationError: If the object could not be validated. :Returns: **The validated model instance.** .. py:method:: model_validate_json(json_data, *, strict = None, context = None) :classmethod: Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing Validate the given JSON data against the Pydantic model. :param json_data: The JSON data to validate. :param strict: Whether to enforce types strictly. :param context: Extra variables to pass to the validator. :Returns: **The validated Pydantic model.** :raises ValidationError: If `json_data` is not a JSON string or the object could not be validated. .. py:method:: model_validate_strings(obj, *, strict = None, context = None) :classmethod: Validate the given object with string data against the Pydantic model. :param obj: The object containing string data to validate. :param strict: Whether to enforce types strictly. :param context: Extra variables to pass to the validator. :Returns: **The validated Pydantic model.** .. py:method:: __get_pydantic_core_schema__(source, handler, /) :classmethod: Hook into generating the model's CoreSchema. :param source: The class we are generating a schema for. This will generally be the same as the `cls` argument if this is a classmethod. :param handler: A callable that calls into Pydantic's internal CoreSchema generation logic. :Returns: **A `pydantic-core` `CoreSchema`.** .. py:method:: __get_pydantic_json_schema__(core_schema, handler, /) :classmethod: Hook into generating the model's JSON schema. :param core_schema: A `pydantic-core` CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`), or just call the handler with the original schema. :param handler: Call into Pydantic's internal JSON schema generation. This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema generation fails. Since this gets called by `BaseModel.model_json_schema` you can override the `schema_generator` argument to that function to change JSON schema generation globally for a type. :Returns: **A JSON schema, as a Python object.** .. py:method:: __pydantic_init_subclass__(**kwargs) :classmethod: This is intended to behave just like `__init_subclass__`, but is called by `ModelMetaclass` only after the class is actually fully initialized. In particular, attributes like `model_fields` will be present when this is called. This is necessary because `__init_subclass__` will always be called by `type.__new__`, and it would require a prohibitively large refactor to the `ModelMetaclass` to ensure that `type.__new__` was called in such a manner that the class would already be sufficiently initialized. This will receive the same `kwargs` that would be passed to the standard `__init_subclass__`, namely, any kwargs passed to the class definition that aren't used internally by pydantic. :param \*\*kwargs: Any keyword arguments passed to the class definition that aren't used internally by pydantic. .. py:method:: __class_getitem__(typevar_values) :classmethod: .. py:method:: __copy__() Returns a shallow copy of the model. .. py:method:: __deepcopy__(memo = None) Returns a deep copy of the model. .. py:method:: __getattr__(item) .. py:method:: _check_frozen(name, value) .. py:method:: __getstate__() .. py:method:: __setstate__(state) .. py:method:: __eq__(other) .. py:method:: __init_subclass__(**kwargs) :classmethod: This signature is included purely to help type-checkers check arguments to class declaration, which provides a way to conveniently set model_config key/value pairs. ```py from pydantic import BaseModel class MyModel(BaseModel, extra='allow'): ... ``` However, this may be deceiving, since the _actual_ calls to `__init_subclass__` will not receive any of the config arguments, and will only receive any keyword arguments passed during class initialization that are _not_ expected keys in ConfigDict. (This is due to the way `ModelMetaclass.__new__` works.) :param \*\*kwargs: Keyword arguments passed to the class definition, which set model_config .. note:: You may want to override `__pydantic_init_subclass__` instead, which behaves similarly but is called *after* the class is fully initialized. .. py:method:: __iter__() So `dict(model)` works. .. py:method:: __repr__() .. py:method:: __repr_args__() .. py:attribute:: __repr_name__ .. py:attribute:: __repr_str__ .. py:attribute:: __pretty__ .. py:attribute:: __rich_repr__ .. py:method:: __str__() .. py:property:: __fields__ :type: dict[str, pydantic.fields.FieldInfo] .. py:property:: __fields_set__ :type: set[str] .. py:method:: dict(*, include = None, exclude = None, by_alias = False, exclude_unset = False, exclude_defaults = False, exclude_none = False) .. py:method:: json(*, include = None, exclude = None, by_alias = False, exclude_unset = False, exclude_defaults = False, exclude_none = False, encoder = PydanticUndefined, models_as_dict = PydanticUndefined, **dumps_kwargs) .. py:method:: parse_obj(obj) :classmethod: .. py:method:: parse_raw(b, *, content_type = None, encoding = 'utf8', proto = None, allow_pickle = False) :classmethod: .. py:method:: parse_file(path, *, content_type = None, encoding = 'utf8', proto = None, allow_pickle = False) :classmethod: .. py:method:: from_orm(obj) :classmethod: .. py:method:: construct(_fields_set = None, **values) :classmethod: .. py:method:: copy(*, include = None, exclude = None, update = None, deep = False) 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) ``` :param include: Optional set or mapping specifying which fields to include in the copied model. :param exclude: Optional set or mapping specifying which fields to exclude in the copied model. :param update: Optional dictionary of field-value pairs to override field values in the copied model. :param 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.** .. py:method:: schema(by_alias = True, ref_template = DEFAULT_REF_TEMPLATE) :classmethod: .. py:method:: schema_json(*, by_alias = True, ref_template = DEFAULT_REF_TEMPLATE, **dumps_kwargs) :classmethod: .. py:method:: validate(value) :classmethod: .. py:method:: update_forward_refs(**localns) :classmethod: .. py:method:: _iter(*args, **kwargs) .. py:method:: _copy_and_set_values(*args, **kwargs) .. py:method:: _get_value(*args, **kwargs) :classmethod: .. py:method:: _calculate_keys(*args, **kwargs) .. py:class:: TargetedPhraseChanger(/, **data) Bases: :py:obj:`pydantic.BaseModel` The target phrase changer. It includes the phrase changer (for preprocessing) along with the relevant concept and the placeholder it will replace. .. py:attribute:: changer :type: PhraseChanger .. py:attribute:: placeholder :type: str .. py:attribute:: cui :type: str .. py:attribute:: onlyprefnames :type: bool .. py:attribute:: model_config :type: ClassVar[pydantic.config.ConfigDict] Configuration for the model, should be a dictionary conforming to [`ConfigDict`][pydantic.config.ConfigDict]. .. py:attribute:: model_fields :type: ClassVar[Dict[str, pydantic.fields.FieldInfo]] Metadata about the fields defined on the model, mapping of field names to [`FieldInfo`][pydantic.fields.FieldInfo] objects. This replaces `Model.__fields__` from Pydantic V1. .. py:attribute:: model_computed_fields :type: ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]] A dictionary of computed field names and their corresponding `ComputedFieldInfo` objects. .. py:attribute:: __class_vars__ :type: ClassVar[set[str]] The names of the class variables defined on the model. .. py:attribute:: __private_attributes__ :type: ClassVar[Dict[str, pydantic.fields.ModelPrivateAttr]] Metadata about the private attributes of the model. .. py:attribute:: __signature__ :type: ClassVar[inspect.Signature] The synthesized `__init__` [`Signature`][inspect.Signature] of the model. .. py:attribute:: __pydantic_complete__ :type: ClassVar[bool] :value: False Whether model building is completed, or if there are still undefined fields. .. py:attribute:: __pydantic_core_schema__ :type: ClassVar[pydantic_core.CoreSchema] The core schema of the model. .. py:attribute:: __pydantic_custom_init__ :type: ClassVar[bool] Whether the model has a custom `__init__` method. .. py:attribute:: __pydantic_decorators__ :type: ClassVar[pydantic._internal._decorators.DecoratorInfos] Metadata containing the decorators defined on the model. This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1. .. py:attribute:: __pydantic_generic_metadata__ :type: ClassVar[pydantic._internal._generics.PydanticGenericMetadata] Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these. .. py:attribute:: __pydantic_parent_namespace__ :type: ClassVar[Dict[str, Any] | None] :value: None Parent namespace of the model, used for automatic rebuilding of models. .. py:attribute:: __pydantic_post_init__ :type: ClassVar[None | Literal['model_post_init']] The name of the post-init method for the model, if defined. .. py:attribute:: __pydantic_root_model__ :type: ClassVar[bool] :value: False Whether the model is a [`RootModel`][pydantic.root_model.RootModel]. .. py:attribute:: __pydantic_serializer__ :type: ClassVar[pydantic_core.SchemaSerializer] The `pydantic-core` `SchemaSerializer` used to dump instances of the model. .. py:attribute:: __pydantic_validator__ :type: ClassVar[pydantic_core.SchemaValidator | pydantic.plugin._schema_validator.PluggableSchemaValidator] The `pydantic-core` `SchemaValidator` used to validate instances of the model. .. py:attribute:: __pydantic_extra__ :type: dict[str, Any] | None A dictionary containing extra values, if [`extra`][pydantic.config.ConfigDict.extra] is set to `'allow'`. .. py:attribute:: __pydantic_fields_set__ :type: set[str] The names of fields explicitly set during instantiation. .. py:attribute:: __pydantic_private__ :type: dict[str, Any] | None Values of private attributes set on the model instance. .. py:attribute:: __slots__ :value: ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__') .. py:method:: __init__(/, **data) 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. .. py:property:: model_extra :type: dict[str, Any] | None Get extra fields set during validation. :Returns: **A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.** .. py:property:: model_fields_set :type: set[str] 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. .. py:method:: model_construct(_fields_set = None, **values) :classmethod: 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. !!! note `model_construct()` generally respects the `model_config.extra` setting on the provided model. That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__` and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored. Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in an error if extra values are passed, but they will be ignored. :param _fields_set: A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the `values` argument will be used. :param values: Trusted or pre-validated data dictionary. :Returns: **A new instance of the `Model` class with validated data.** .. py:method:: model_copy(*, update = None, deep = False) Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy Returns a copy of the model. :param 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. :param deep: Set to `True` to make a deep copy of the model. :Returns: **New model instance.** .. py:method:: model_dump(*, mode = 'python', include = None, exclude = None, context = None, by_alias = False, exclude_unset = False, exclude_defaults = False, exclude_none = False, round_trip = False, warnings = True, serialize_as_any = False) Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump Generate a dictionary representation of the model, optionally specifying which fields to include or exclude. :param mode: The mode in which `to_python` should run. If mode is 'json', the output will only contain JSON serializable types. If mode is 'python', the output may contain non-JSON-serializable Python objects. :param include: A set of fields to include in the output. :param exclude: A set of fields to exclude from the output. :param context: Additional context to pass to the serializer. :param by_alias: Whether to use the field's alias in the dictionary key if defined. :param exclude_unset: Whether to exclude fields that have not been explicitly set. :param exclude_defaults: Whether to exclude fields that are set to their default value. :param exclude_none: Whether to exclude fields that have a value of `None`. :param round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T]. :param warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError]. :param serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. :Returns: **A dictionary representation of the model.** .. py:method:: model_dump_json(*, indent = None, include = None, exclude = None, context = None, by_alias = False, exclude_unset = False, exclude_defaults = False, exclude_none = False, round_trip = False, warnings = True, serialize_as_any = False) Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json Generates a JSON representation of the model using Pydantic's `to_json` method. :param indent: Indentation to use in the JSON output. If None is passed, the output will be compact. :param include: Field(s) to include in the JSON output. :param exclude: Field(s) to exclude from the JSON output. :param context: Additional context to pass to the serializer. :param by_alias: Whether to serialize using field aliases. :param exclude_unset: Whether to exclude fields that have not been explicitly set. :param exclude_defaults: Whether to exclude fields that are set to their default value. :param exclude_none: Whether to exclude fields that have a value of `None`. :param round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T]. :param warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError]. :param serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. :Returns: **A JSON string representation of the model.** .. py:method:: model_json_schema(by_alias = True, ref_template = DEFAULT_REF_TEMPLATE, schema_generator = GenerateJsonSchema, mode = 'validation') :classmethod: Generates a JSON schema for a model class. :param by_alias: Whether to use attribute aliases or not. :param ref_template: The reference template. :param schema_generator: To override the logic used to generate the JSON schema, as a subclass of `GenerateJsonSchema` with your desired modifications :param mode: The mode in which to generate the schema. :Returns: **The JSON schema for the given model class.** .. py:method:: model_parametrized_name(params) :classmethod: Compute the class name for parametrizations of generic classes. This method can be overridden to achieve a custom naming scheme for generic BaseModels. :param 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. .. py:method:: model_post_init(__context) 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. .. py:method:: model_rebuild(*, force = False, raise_errors = True, _parent_namespace_depth = 2, _types_namespace = None) :classmethod: 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. :param force: Whether to force the rebuilding of the model schema, defaults to `False`. :param raise_errors: Whether to raise errors, defaults to `True`. :param _parent_namespace_depth: The depth level of the parent namespace, defaults to 2. :param _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`.** .. py:method:: model_validate(obj, *, strict = None, from_attributes = None, context = None) :classmethod: Validate a pydantic model instance. :param obj: The object to validate. :param strict: Whether to enforce types strictly. :param from_attributes: Whether to extract data from object attributes. :param context: Additional context to pass to the validator. :raises ValidationError: If the object could not be validated. :Returns: **The validated model instance.** .. py:method:: model_validate_json(json_data, *, strict = None, context = None) :classmethod: Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing Validate the given JSON data against the Pydantic model. :param json_data: The JSON data to validate. :param strict: Whether to enforce types strictly. :param context: Extra variables to pass to the validator. :Returns: **The validated Pydantic model.** :raises ValidationError: If `json_data` is not a JSON string or the object could not be validated. .. py:method:: model_validate_strings(obj, *, strict = None, context = None) :classmethod: Validate the given object with string data against the Pydantic model. :param obj: The object containing string data to validate. :param strict: Whether to enforce types strictly. :param context: Extra variables to pass to the validator. :Returns: **The validated Pydantic model.** .. py:method:: __get_pydantic_core_schema__(source, handler, /) :classmethod: Hook into generating the model's CoreSchema. :param source: The class we are generating a schema for. This will generally be the same as the `cls` argument if this is a classmethod. :param handler: A callable that calls into Pydantic's internal CoreSchema generation logic. :Returns: **A `pydantic-core` `CoreSchema`.** .. py:method:: __get_pydantic_json_schema__(core_schema, handler, /) :classmethod: Hook into generating the model's JSON schema. :param core_schema: A `pydantic-core` CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`), or just call the handler with the original schema. :param handler: Call into Pydantic's internal JSON schema generation. This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema generation fails. Since this gets called by `BaseModel.model_json_schema` you can override the `schema_generator` argument to that function to change JSON schema generation globally for a type. :Returns: **A JSON schema, as a Python object.** .. py:method:: __pydantic_init_subclass__(**kwargs) :classmethod: This is intended to behave just like `__init_subclass__`, but is called by `ModelMetaclass` only after the class is actually fully initialized. In particular, attributes like `model_fields` will be present when this is called. This is necessary because `__init_subclass__` will always be called by `type.__new__`, and it would require a prohibitively large refactor to the `ModelMetaclass` to ensure that `type.__new__` was called in such a manner that the class would already be sufficiently initialized. This will receive the same `kwargs` that would be passed to the standard `__init_subclass__`, namely, any kwargs passed to the class definition that aren't used internally by pydantic. :param \*\*kwargs: Any keyword arguments passed to the class definition that aren't used internally by pydantic. .. py:method:: __class_getitem__(typevar_values) :classmethod: .. py:method:: __copy__() Returns a shallow copy of the model. .. py:method:: __deepcopy__(memo = None) Returns a deep copy of the model. .. py:method:: __getattr__(item) .. py:method:: _check_frozen(name, value) .. py:method:: __getstate__() .. py:method:: __setstate__(state) .. py:method:: __eq__(other) .. py:method:: __init_subclass__(**kwargs) :classmethod: This signature is included purely to help type-checkers check arguments to class declaration, which provides a way to conveniently set model_config key/value pairs. ```py from pydantic import BaseModel class MyModel(BaseModel, extra='allow'): ... ``` However, this may be deceiving, since the _actual_ calls to `__init_subclass__` will not receive any of the config arguments, and will only receive any keyword arguments passed during class initialization that are _not_ expected keys in ConfigDict. (This is due to the way `ModelMetaclass.__new__` works.) :param \*\*kwargs: Keyword arguments passed to the class definition, which set model_config .. note:: You may want to override `__pydantic_init_subclass__` instead, which behaves similarly but is called *after* the class is fully initialized. .. py:method:: __iter__() So `dict(model)` works. .. py:method:: __repr__() .. py:method:: __repr_args__() .. py:attribute:: __repr_name__ .. py:attribute:: __repr_str__ .. py:attribute:: __pretty__ .. py:attribute:: __rich_repr__ .. py:method:: __str__() .. py:property:: __fields__ :type: dict[str, pydantic.fields.FieldInfo] .. py:property:: __fields_set__ :type: set[str] .. py:method:: dict(*, include = None, exclude = None, by_alias = False, exclude_unset = False, exclude_defaults = False, exclude_none = False) .. py:method:: json(*, include = None, exclude = None, by_alias = False, exclude_unset = False, exclude_defaults = False, exclude_none = False, encoder = PydanticUndefined, models_as_dict = PydanticUndefined, **dumps_kwargs) .. py:method:: parse_obj(obj) :classmethod: .. py:method:: parse_raw(b, *, content_type = None, encoding = 'utf8', proto = None, allow_pickle = False) :classmethod: .. py:method:: parse_file(path, *, content_type = None, encoding = 'utf8', proto = None, allow_pickle = False) :classmethod: .. py:method:: from_orm(obj) :classmethod: .. py:method:: construct(_fields_set = None, **values) :classmethod: .. py:method:: copy(*, include = None, exclude = None, update = None, deep = False) 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) ``` :param include: Optional set or mapping specifying which fields to include in the copied model. :param exclude: Optional set or mapping specifying which fields to exclude in the copied model. :param update: Optional dictionary of field-value pairs to override field values in the copied model. :param 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.** .. py:method:: schema(by_alias = True, ref_template = DEFAULT_REF_TEMPLATE) :classmethod: .. py:method:: schema_json(*, by_alias = True, ref_template = DEFAULT_REF_TEMPLATE, **dumps_kwargs) :classmethod: .. py:method:: validate(value) :classmethod: .. py:method:: update_forward_refs(**localns) :classmethod: .. py:method:: _iter(*args, **kwargs) .. py:method:: _copy_and_set_values(*args, **kwargs) .. py:method:: _get_value(*args, **kwargs) :classmethod: .. py:method:: _calculate_keys(*args, **kwargs) .. py:class:: FinalTarget(/, **data) Bases: :py:obj:`pydantic.BaseModel` The final target. This involves the final phrase (which (potentially) has other placeholder replaced in it), the placeholder to be replaced, and the CUI and specific name being used. .. py:attribute:: placeholder :type: str .. py:attribute:: cui :type: str .. py:attribute:: name :type: str .. py:attribute:: final_phrase :type: str .. py:attribute:: model_config :type: ClassVar[pydantic.config.ConfigDict] Configuration for the model, should be a dictionary conforming to [`ConfigDict`][pydantic.config.ConfigDict]. .. py:attribute:: model_fields :type: ClassVar[Dict[str, pydantic.fields.FieldInfo]] Metadata about the fields defined on the model, mapping of field names to [`FieldInfo`][pydantic.fields.FieldInfo] objects. This replaces `Model.__fields__` from Pydantic V1. .. py:attribute:: model_computed_fields :type: ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]] A dictionary of computed field names and their corresponding `ComputedFieldInfo` objects. .. py:attribute:: __class_vars__ :type: ClassVar[set[str]] The names of the class variables defined on the model. .. py:attribute:: __private_attributes__ :type: ClassVar[Dict[str, pydantic.fields.ModelPrivateAttr]] Metadata about the private attributes of the model. .. py:attribute:: __signature__ :type: ClassVar[inspect.Signature] The synthesized `__init__` [`Signature`][inspect.Signature] of the model. .. py:attribute:: __pydantic_complete__ :type: ClassVar[bool] :value: False Whether model building is completed, or if there are still undefined fields. .. py:attribute:: __pydantic_core_schema__ :type: ClassVar[pydantic_core.CoreSchema] The core schema of the model. .. py:attribute:: __pydantic_custom_init__ :type: ClassVar[bool] Whether the model has a custom `__init__` method. .. py:attribute:: __pydantic_decorators__ :type: ClassVar[pydantic._internal._decorators.DecoratorInfos] Metadata containing the decorators defined on the model. This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1. .. py:attribute:: __pydantic_generic_metadata__ :type: ClassVar[pydantic._internal._generics.PydanticGenericMetadata] Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these. .. py:attribute:: __pydantic_parent_namespace__ :type: ClassVar[Dict[str, Any] | None] :value: None Parent namespace of the model, used for automatic rebuilding of models. .. py:attribute:: __pydantic_post_init__ :type: ClassVar[None | Literal['model_post_init']] The name of the post-init method for the model, if defined. .. py:attribute:: __pydantic_root_model__ :type: ClassVar[bool] :value: False Whether the model is a [`RootModel`][pydantic.root_model.RootModel]. .. py:attribute:: __pydantic_serializer__ :type: ClassVar[pydantic_core.SchemaSerializer] The `pydantic-core` `SchemaSerializer` used to dump instances of the model. .. py:attribute:: __pydantic_validator__ :type: ClassVar[pydantic_core.SchemaValidator | pydantic.plugin._schema_validator.PluggableSchemaValidator] The `pydantic-core` `SchemaValidator` used to validate instances of the model. .. py:attribute:: __pydantic_extra__ :type: dict[str, Any] | None A dictionary containing extra values, if [`extra`][pydantic.config.ConfigDict.extra] is set to `'allow'`. .. py:attribute:: __pydantic_fields_set__ :type: set[str] The names of fields explicitly set during instantiation. .. py:attribute:: __pydantic_private__ :type: dict[str, Any] | None Values of private attributes set on the model instance. .. py:attribute:: __slots__ :value: ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__') .. py:method:: __init__(/, **data) 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. .. py:property:: model_extra :type: dict[str, Any] | None Get extra fields set during validation. :Returns: **A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.** .. py:property:: model_fields_set :type: set[str] 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. .. py:method:: model_construct(_fields_set = None, **values) :classmethod: 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. !!! note `model_construct()` generally respects the `model_config.extra` setting on the provided model. That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__` and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored. Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in an error if extra values are passed, but they will be ignored. :param _fields_set: A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the `values` argument will be used. :param values: Trusted or pre-validated data dictionary. :Returns: **A new instance of the `Model` class with validated data.** .. py:method:: model_copy(*, update = None, deep = False) Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy Returns a copy of the model. :param 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. :param deep: Set to `True` to make a deep copy of the model. :Returns: **New model instance.** .. py:method:: model_dump(*, mode = 'python', include = None, exclude = None, context = None, by_alias = False, exclude_unset = False, exclude_defaults = False, exclude_none = False, round_trip = False, warnings = True, serialize_as_any = False) Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump Generate a dictionary representation of the model, optionally specifying which fields to include or exclude. :param mode: The mode in which `to_python` should run. If mode is 'json', the output will only contain JSON serializable types. If mode is 'python', the output may contain non-JSON-serializable Python objects. :param include: A set of fields to include in the output. :param exclude: A set of fields to exclude from the output. :param context: Additional context to pass to the serializer. :param by_alias: Whether to use the field's alias in the dictionary key if defined. :param exclude_unset: Whether to exclude fields that have not been explicitly set. :param exclude_defaults: Whether to exclude fields that are set to their default value. :param exclude_none: Whether to exclude fields that have a value of `None`. :param round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T]. :param warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError]. :param serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. :Returns: **A dictionary representation of the model.** .. py:method:: model_dump_json(*, indent = None, include = None, exclude = None, context = None, by_alias = False, exclude_unset = False, exclude_defaults = False, exclude_none = False, round_trip = False, warnings = True, serialize_as_any = False) Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json Generates a JSON representation of the model using Pydantic's `to_json` method. :param indent: Indentation to use in the JSON output. If None is passed, the output will be compact. :param include: Field(s) to include in the JSON output. :param exclude: Field(s) to exclude from the JSON output. :param context: Additional context to pass to the serializer. :param by_alias: Whether to serialize using field aliases. :param exclude_unset: Whether to exclude fields that have not been explicitly set. :param exclude_defaults: Whether to exclude fields that are set to their default value. :param exclude_none: Whether to exclude fields that have a value of `None`. :param round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T]. :param warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError]. :param serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. :Returns: **A JSON string representation of the model.** .. py:method:: model_json_schema(by_alias = True, ref_template = DEFAULT_REF_TEMPLATE, schema_generator = GenerateJsonSchema, mode = 'validation') :classmethod: Generates a JSON schema for a model class. :param by_alias: Whether to use attribute aliases or not. :param ref_template: The reference template. :param schema_generator: To override the logic used to generate the JSON schema, as a subclass of `GenerateJsonSchema` with your desired modifications :param mode: The mode in which to generate the schema. :Returns: **The JSON schema for the given model class.** .. py:method:: model_parametrized_name(params) :classmethod: Compute the class name for parametrizations of generic classes. This method can be overridden to achieve a custom naming scheme for generic BaseModels. :param 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. .. py:method:: model_post_init(__context) 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. .. py:method:: model_rebuild(*, force = False, raise_errors = True, _parent_namespace_depth = 2, _types_namespace = None) :classmethod: 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. :param force: Whether to force the rebuilding of the model schema, defaults to `False`. :param raise_errors: Whether to raise errors, defaults to `True`. :param _parent_namespace_depth: The depth level of the parent namespace, defaults to 2. :param _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`.** .. py:method:: model_validate(obj, *, strict = None, from_attributes = None, context = None) :classmethod: Validate a pydantic model instance. :param obj: The object to validate. :param strict: Whether to enforce types strictly. :param from_attributes: Whether to extract data from object attributes. :param context: Additional context to pass to the validator. :raises ValidationError: If the object could not be validated. :Returns: **The validated model instance.** .. py:method:: model_validate_json(json_data, *, strict = None, context = None) :classmethod: Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing Validate the given JSON data against the Pydantic model. :param json_data: The JSON data to validate. :param strict: Whether to enforce types strictly. :param context: Extra variables to pass to the validator. :Returns: **The validated Pydantic model.** :raises ValidationError: If `json_data` is not a JSON string or the object could not be validated. .. py:method:: model_validate_strings(obj, *, strict = None, context = None) :classmethod: Validate the given object with string data against the Pydantic model. :param obj: The object containing string data to validate. :param strict: Whether to enforce types strictly. :param context: Extra variables to pass to the validator. :Returns: **The validated Pydantic model.** .. py:method:: __get_pydantic_core_schema__(source, handler, /) :classmethod: Hook into generating the model's CoreSchema. :param source: The class we are generating a schema for. This will generally be the same as the `cls` argument if this is a classmethod. :param handler: A callable that calls into Pydantic's internal CoreSchema generation logic. :Returns: **A `pydantic-core` `CoreSchema`.** .. py:method:: __get_pydantic_json_schema__(core_schema, handler, /) :classmethod: Hook into generating the model's JSON schema. :param core_schema: A `pydantic-core` CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`), or just call the handler with the original schema. :param handler: Call into Pydantic's internal JSON schema generation. This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema generation fails. Since this gets called by `BaseModel.model_json_schema` you can override the `schema_generator` argument to that function to change JSON schema generation globally for a type. :Returns: **A JSON schema, as a Python object.** .. py:method:: __pydantic_init_subclass__(**kwargs) :classmethod: This is intended to behave just like `__init_subclass__`, but is called by `ModelMetaclass` only after the class is actually fully initialized. In particular, attributes like `model_fields` will be present when this is called. This is necessary because `__init_subclass__` will always be called by `type.__new__`, and it would require a prohibitively large refactor to the `ModelMetaclass` to ensure that `type.__new__` was called in such a manner that the class would already be sufficiently initialized. This will receive the same `kwargs` that would be passed to the standard `__init_subclass__`, namely, any kwargs passed to the class definition that aren't used internally by pydantic. :param \*\*kwargs: Any keyword arguments passed to the class definition that aren't used internally by pydantic. .. py:method:: __class_getitem__(typevar_values) :classmethod: .. py:method:: __copy__() Returns a shallow copy of the model. .. py:method:: __deepcopy__(memo = None) Returns a deep copy of the model. .. py:method:: __getattr__(item) .. py:method:: _check_frozen(name, value) .. py:method:: __getstate__() .. py:method:: __setstate__(state) .. py:method:: __eq__(other) .. py:method:: __init_subclass__(**kwargs) :classmethod: This signature is included purely to help type-checkers check arguments to class declaration, which provides a way to conveniently set model_config key/value pairs. ```py from pydantic import BaseModel class MyModel(BaseModel, extra='allow'): ... ``` However, this may be deceiving, since the _actual_ calls to `__init_subclass__` will not receive any of the config arguments, and will only receive any keyword arguments passed during class initialization that are _not_ expected keys in ConfigDict. (This is due to the way `ModelMetaclass.__new__` works.) :param \*\*kwargs: Keyword arguments passed to the class definition, which set model_config .. note:: You may want to override `__pydantic_init_subclass__` instead, which behaves similarly but is called *after* the class is fully initialized. .. py:method:: __iter__() So `dict(model)` works. .. py:method:: __repr__() .. py:method:: __repr_args__() .. py:attribute:: __repr_name__ .. py:attribute:: __repr_str__ .. py:attribute:: __pretty__ .. py:attribute:: __rich_repr__ .. py:method:: __str__() .. py:property:: __fields__ :type: dict[str, pydantic.fields.FieldInfo] .. py:property:: __fields_set__ :type: set[str] .. py:method:: dict(*, include = None, exclude = None, by_alias = False, exclude_unset = False, exclude_defaults = False, exclude_none = False) .. py:method:: json(*, include = None, exclude = None, by_alias = False, exclude_unset = False, exclude_defaults = False, exclude_none = False, encoder = PydanticUndefined, models_as_dict = PydanticUndefined, **dumps_kwargs) .. py:method:: parse_obj(obj) :classmethod: .. py:method:: parse_raw(b, *, content_type = None, encoding = 'utf8', proto = None, allow_pickle = False) :classmethod: .. py:method:: parse_file(path, *, content_type = None, encoding = 'utf8', proto = None, allow_pickle = False) :classmethod: .. py:method:: from_orm(obj) :classmethod: .. py:method:: construct(_fields_set = None, **values) :classmethod: .. py:method:: copy(*, include = None, exclude = None, update = None, deep = False) 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) ``` :param include: Optional set or mapping specifying which fields to include in the copied model. :param exclude: Optional set or mapping specifying which fields to exclude in the copied model. :param update: Optional dictionary of field-value pairs to override field values in the copied model. :param 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.** .. py:method:: schema(by_alias = True, ref_template = DEFAULT_REF_TEMPLATE) :classmethod: .. py:method:: schema_json(*, by_alias = True, ref_template = DEFAULT_REF_TEMPLATE, **dumps_kwargs) :classmethod: .. py:method:: validate(value) :classmethod: .. py:method:: update_forward_refs(**localns) :classmethod: .. py:method:: _iter(*args, **kwargs) .. py:method:: _copy_and_set_values(*args, **kwargs) .. py:method:: _get_value(*args, **kwargs) :classmethod: .. py:method:: _calculate_keys(*args, **kwargs) .. py:class:: OptionSet(/, **data) Bases: :py:obj:`pydantic.BaseModel` The targeting option set. This describes all the target placeholders and concepts needed. .. py:attribute:: options :type: list[TargetPlaceholder] .. py:attribute:: allow_any_combinations :type: bool :value: False .. py:method:: from_dict(section) :classmethod: Construct a OptionSet instance from a dict. The assumed structure is: { 'placeholders': [ { 'placeholder': , 'cuis': , 'prefname-only': 'true' }, ], 'any-combination': } The prefname-only key is optional. :param section: The dict to parse :type section: dict[str, Any] :raises ProblematicOptionSetException: If incorrect number of CUIs when not allowing any combination :raises ProblematicOptionSetException: If placeholders not a list :raises ProblematicOptionSetException: If multiple placehodlers with same place holder :Returns: **OptionSet** -- The resulting OptionSet .. py:method:: to_dict() Convert the OptionSet to a dict. :Returns: **dict** -- The dict representation .. py:method:: _get_all_combinations(cur_opts, other_opts, translation) .. py:method:: estimate_num_of_subcases() Get the number of distinct subcases. This includes ones that can be calculated without the knowledge of the underlying CDB. I.e it doesn't care for the number of names involved per CUI but only takes into account what is described in the option set itself. If any combination is allowed, then the answer is the combination of the number of target concepts per option. If any combination is not allowed, then the answer is simply the number of target concepts for an option (they should all have the same number). :Returns: **int** -- Te number of subcases. .. py:method:: get_preprocessors_and_targets(translation) Get the targeted phrase changers. :param translation: The translaton layer. :type translation: TranslationLayer :Yields: *Iterator[TargetedPhraseChanger]* -- Thetarget phrase changers. .. py:attribute:: model_config :type: ClassVar[pydantic.config.ConfigDict] Configuration for the model, should be a dictionary conforming to [`ConfigDict`][pydantic.config.ConfigDict]. .. py:attribute:: model_fields :type: ClassVar[Dict[str, pydantic.fields.FieldInfo]] Metadata about the fields defined on the model, mapping of field names to [`FieldInfo`][pydantic.fields.FieldInfo] objects. This replaces `Model.__fields__` from Pydantic V1. .. py:attribute:: model_computed_fields :type: ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]] A dictionary of computed field names and their corresponding `ComputedFieldInfo` objects. .. py:attribute:: __class_vars__ :type: ClassVar[set[str]] The names of the class variables defined on the model. .. py:attribute:: __private_attributes__ :type: ClassVar[Dict[str, pydantic.fields.ModelPrivateAttr]] Metadata about the private attributes of the model. .. py:attribute:: __signature__ :type: ClassVar[inspect.Signature] The synthesized `__init__` [`Signature`][inspect.Signature] of the model. .. py:attribute:: __pydantic_complete__ :type: ClassVar[bool] :value: False Whether model building is completed, or if there are still undefined fields. .. py:attribute:: __pydantic_core_schema__ :type: ClassVar[pydantic_core.CoreSchema] The core schema of the model. .. py:attribute:: __pydantic_custom_init__ :type: ClassVar[bool] Whether the model has a custom `__init__` method. .. py:attribute:: __pydantic_decorators__ :type: ClassVar[pydantic._internal._decorators.DecoratorInfos] Metadata containing the decorators defined on the model. This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1. .. py:attribute:: __pydantic_generic_metadata__ :type: ClassVar[pydantic._internal._generics.PydanticGenericMetadata] Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these. .. py:attribute:: __pydantic_parent_namespace__ :type: ClassVar[Dict[str, Any] | None] :value: None Parent namespace of the model, used for automatic rebuilding of models. .. py:attribute:: __pydantic_post_init__ :type: ClassVar[None | Literal['model_post_init']] The name of the post-init method for the model, if defined. .. py:attribute:: __pydantic_root_model__ :type: ClassVar[bool] :value: False Whether the model is a [`RootModel`][pydantic.root_model.RootModel]. .. py:attribute:: __pydantic_serializer__ :type: ClassVar[pydantic_core.SchemaSerializer] The `pydantic-core` `SchemaSerializer` used to dump instances of the model. .. py:attribute:: __pydantic_validator__ :type: ClassVar[pydantic_core.SchemaValidator | pydantic.plugin._schema_validator.PluggableSchemaValidator] The `pydantic-core` `SchemaValidator` used to validate instances of the model. .. py:attribute:: __pydantic_extra__ :type: dict[str, Any] | None A dictionary containing extra values, if [`extra`][pydantic.config.ConfigDict.extra] is set to `'allow'`. .. py:attribute:: __pydantic_fields_set__ :type: set[str] The names of fields explicitly set during instantiation. .. py:attribute:: __pydantic_private__ :type: dict[str, Any] | None Values of private attributes set on the model instance. .. py:attribute:: __slots__ :value: ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__') .. py:method:: __init__(/, **data) 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. .. py:property:: model_extra :type: dict[str, Any] | None Get extra fields set during validation. :Returns: **A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.** .. py:property:: model_fields_set :type: set[str] 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. .. py:method:: model_construct(_fields_set = None, **values) :classmethod: 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. !!! note `model_construct()` generally respects the `model_config.extra` setting on the provided model. That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__` and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored. Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in an error if extra values are passed, but they will be ignored. :param _fields_set: A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the `values` argument will be used. :param values: Trusted or pre-validated data dictionary. :Returns: **A new instance of the `Model` class with validated data.** .. py:method:: model_copy(*, update = None, deep = False) Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy Returns a copy of the model. :param 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. :param deep: Set to `True` to make a deep copy of the model. :Returns: **New model instance.** .. py:method:: model_dump(*, mode = 'python', include = None, exclude = None, context = None, by_alias = False, exclude_unset = False, exclude_defaults = False, exclude_none = False, round_trip = False, warnings = True, serialize_as_any = False) Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump Generate a dictionary representation of the model, optionally specifying which fields to include or exclude. :param mode: The mode in which `to_python` should run. If mode is 'json', the output will only contain JSON serializable types. If mode is 'python', the output may contain non-JSON-serializable Python objects. :param include: A set of fields to include in the output. :param exclude: A set of fields to exclude from the output. :param context: Additional context to pass to the serializer. :param by_alias: Whether to use the field's alias in the dictionary key if defined. :param exclude_unset: Whether to exclude fields that have not been explicitly set. :param exclude_defaults: Whether to exclude fields that are set to their default value. :param exclude_none: Whether to exclude fields that have a value of `None`. :param round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T]. :param warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError]. :param serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. :Returns: **A dictionary representation of the model.** .. py:method:: model_dump_json(*, indent = None, include = None, exclude = None, context = None, by_alias = False, exclude_unset = False, exclude_defaults = False, exclude_none = False, round_trip = False, warnings = True, serialize_as_any = False) Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json Generates a JSON representation of the model using Pydantic's `to_json` method. :param indent: Indentation to use in the JSON output. If None is passed, the output will be compact. :param include: Field(s) to include in the JSON output. :param exclude: Field(s) to exclude from the JSON output. :param context: Additional context to pass to the serializer. :param by_alias: Whether to serialize using field aliases. :param exclude_unset: Whether to exclude fields that have not been explicitly set. :param exclude_defaults: Whether to exclude fields that are set to their default value. :param exclude_none: Whether to exclude fields that have a value of `None`. :param round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T]. :param warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError]. :param serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. :Returns: **A JSON string representation of the model.** .. py:method:: model_json_schema(by_alias = True, ref_template = DEFAULT_REF_TEMPLATE, schema_generator = GenerateJsonSchema, mode = 'validation') :classmethod: Generates a JSON schema for a model class. :param by_alias: Whether to use attribute aliases or not. :param ref_template: The reference template. :param schema_generator: To override the logic used to generate the JSON schema, as a subclass of `GenerateJsonSchema` with your desired modifications :param mode: The mode in which to generate the schema. :Returns: **The JSON schema for the given model class.** .. py:method:: model_parametrized_name(params) :classmethod: Compute the class name for parametrizations of generic classes. This method can be overridden to achieve a custom naming scheme for generic BaseModels. :param 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. .. py:method:: model_post_init(__context) 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. .. py:method:: model_rebuild(*, force = False, raise_errors = True, _parent_namespace_depth = 2, _types_namespace = None) :classmethod: 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. :param force: Whether to force the rebuilding of the model schema, defaults to `False`. :param raise_errors: Whether to raise errors, defaults to `True`. :param _parent_namespace_depth: The depth level of the parent namespace, defaults to 2. :param _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`.** .. py:method:: model_validate(obj, *, strict = None, from_attributes = None, context = None) :classmethod: Validate a pydantic model instance. :param obj: The object to validate. :param strict: Whether to enforce types strictly. :param from_attributes: Whether to extract data from object attributes. :param context: Additional context to pass to the validator. :raises ValidationError: If the object could not be validated. :Returns: **The validated model instance.** .. py:method:: model_validate_json(json_data, *, strict = None, context = None) :classmethod: Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing Validate the given JSON data against the Pydantic model. :param json_data: The JSON data to validate. :param strict: Whether to enforce types strictly. :param context: Extra variables to pass to the validator. :Returns: **The validated Pydantic model.** :raises ValidationError: If `json_data` is not a JSON string or the object could not be validated. .. py:method:: model_validate_strings(obj, *, strict = None, context = None) :classmethod: Validate the given object with string data against the Pydantic model. :param obj: The object containing string data to validate. :param strict: Whether to enforce types strictly. :param context: Extra variables to pass to the validator. :Returns: **The validated Pydantic model.** .. py:method:: __get_pydantic_core_schema__(source, handler, /) :classmethod: Hook into generating the model's CoreSchema. :param source: The class we are generating a schema for. This will generally be the same as the `cls` argument if this is a classmethod. :param handler: A callable that calls into Pydantic's internal CoreSchema generation logic. :Returns: **A `pydantic-core` `CoreSchema`.** .. py:method:: __get_pydantic_json_schema__(core_schema, handler, /) :classmethod: Hook into generating the model's JSON schema. :param core_schema: A `pydantic-core` CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`), or just call the handler with the original schema. :param handler: Call into Pydantic's internal JSON schema generation. This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema generation fails. Since this gets called by `BaseModel.model_json_schema` you can override the `schema_generator` argument to that function to change JSON schema generation globally for a type. :Returns: **A JSON schema, as a Python object.** .. py:method:: __pydantic_init_subclass__(**kwargs) :classmethod: This is intended to behave just like `__init_subclass__`, but is called by `ModelMetaclass` only after the class is actually fully initialized. In particular, attributes like `model_fields` will be present when this is called. This is necessary because `__init_subclass__` will always be called by `type.__new__`, and it would require a prohibitively large refactor to the `ModelMetaclass` to ensure that `type.__new__` was called in such a manner that the class would already be sufficiently initialized. This will receive the same `kwargs` that would be passed to the standard `__init_subclass__`, namely, any kwargs passed to the class definition that aren't used internally by pydantic. :param \*\*kwargs: Any keyword arguments passed to the class definition that aren't used internally by pydantic. .. py:method:: __class_getitem__(typevar_values) :classmethod: .. py:method:: __copy__() Returns a shallow copy of the model. .. py:method:: __deepcopy__(memo = None) Returns a deep copy of the model. .. py:method:: __getattr__(item) .. py:method:: _check_frozen(name, value) .. py:method:: __getstate__() .. py:method:: __setstate__(state) .. py:method:: __eq__(other) .. py:method:: __init_subclass__(**kwargs) :classmethod: This signature is included purely to help type-checkers check arguments to class declaration, which provides a way to conveniently set model_config key/value pairs. ```py from pydantic import BaseModel class MyModel(BaseModel, extra='allow'): ... ``` However, this may be deceiving, since the _actual_ calls to `__init_subclass__` will not receive any of the config arguments, and will only receive any keyword arguments passed during class initialization that are _not_ expected keys in ConfigDict. (This is due to the way `ModelMetaclass.__new__` works.) :param \*\*kwargs: Keyword arguments passed to the class definition, which set model_config .. note:: You may want to override `__pydantic_init_subclass__` instead, which behaves similarly but is called *after* the class is fully initialized. .. py:method:: __iter__() So `dict(model)` works. .. py:method:: __repr__() .. py:method:: __repr_args__() .. py:attribute:: __repr_name__ .. py:attribute:: __repr_str__ .. py:attribute:: __pretty__ .. py:attribute:: __rich_repr__ .. py:method:: __str__() .. py:property:: __fields__ :type: dict[str, pydantic.fields.FieldInfo] .. py:property:: __fields_set__ :type: set[str] .. py:method:: dict(*, include = None, exclude = None, by_alias = False, exclude_unset = False, exclude_defaults = False, exclude_none = False) .. py:method:: json(*, include = None, exclude = None, by_alias = False, exclude_unset = False, exclude_defaults = False, exclude_none = False, encoder = PydanticUndefined, models_as_dict = PydanticUndefined, **dumps_kwargs) .. py:method:: parse_obj(obj) :classmethod: .. py:method:: parse_raw(b, *, content_type = None, encoding = 'utf8', proto = None, allow_pickle = False) :classmethod: .. py:method:: parse_file(path, *, content_type = None, encoding = 'utf8', proto = None, allow_pickle = False) :classmethod: .. py:method:: from_orm(obj) :classmethod: .. py:method:: construct(_fields_set = None, **values) :classmethod: .. py:method:: copy(*, include = None, exclude = None, update = None, deep = False) 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) ``` :param include: Optional set or mapping specifying which fields to include in the copied model. :param exclude: Optional set or mapping specifying which fields to exclude in the copied model. :param update: Optional dictionary of field-value pairs to override field values in the copied model. :param 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.** .. py:method:: schema(by_alias = True, ref_template = DEFAULT_REF_TEMPLATE) :classmethod: .. py:method:: schema_json(*, by_alias = True, ref_template = DEFAULT_REF_TEMPLATE, **dumps_kwargs) :classmethod: .. py:method:: validate(value) :classmethod: .. py:method:: update_forward_refs(**localns) :classmethod: .. py:method:: _iter(*args, **kwargs) .. py:method:: _copy_and_set_values(*args, **kwargs) .. py:method:: _get_value(*args, **kwargs) :classmethod: .. py:method:: _calculate_keys(*args, **kwargs) .. py:exception:: ProblematicOptionSetException(*args) Bases: :py:obj:`ValueError` Inappropriate argument value (of correct type). .. py:method:: __init__(*args) Initialize self. See help(type(self)) for accurate signature. .. py:class:: __cause__ exception cause .. py:class:: __context__ exception context .. py:method:: __delattr__() Implement delattr(self, name). .. py:method:: __dir__() Default dir() implementation. .. py:method:: __eq__() Return self==value. .. py:method:: __format__() Default object formatter. .. py:method:: __ge__() Return self>=value. .. py:method:: __getattribute__() Return getattr(self, name). .. py:method:: __gt__() Return self>value. .. py:method:: __hash__() Return hash(self). .. py:method:: __le__() Return self<=value. .. py:method:: __lt__() Return self