ModelDTO class.
firstname: str
id: str
lastname: str
photo: Optional
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.
Any
Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use model_copy
instead.
If you need include
or exclude
, use:
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
Args: include: Optional set or mapping specifying which fields to include in the copied model. exclude: Optional set or mapping specifying which fields to exclude in the copied model. update: Optional dictionary of field-value pairs to override field values in the copied model. deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns: A copy of the model with included, excluded and updated fields as specified.
AbstractSetIntStr | MappingIntStrAny | None
AbstractSetIntStr | MappingIntStrAny | None
Dict[str, Any] | None
bool
- False
Model
IncEx
IncEx
bool
- False
bool
- False
bool
- False
bool
- False
Dict[str, Any]
IncEx
IncEx
bool
- False
bool
- False
bool
- False
bool
- False
Callable[[Any], Any] | None
- PydanticUndefined
bool
- PydanticUndefined
Any
str
Usage docs: https://docs.pydantic.dev/2.7/concepts/serialization/#model_copy
Returns a copy of the model.
Args:
update: Values to change/add in the new model. Note: the data is not validated
before creating the new model. You should trust this data.
deep: Set to True
to make a deep copy of the model.
Returns: New model instance.
dict[str, Any] | None
bool
- False
Model
Usage docs: https://docs.pydantic.dev/2.7/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Args:
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.
include: A set of fields to include in the output.
exclude: A set of fields to exclude from the output.
context: Additional context to pass to the serializer.
by_alias: Whether to use the field's alias in the dictionary key if defined.
exclude_unset: Whether to exclude fields that have not been explicitly set.
exclude_defaults: Whether to exclude fields that are set to their default value.
exclude_none: Whether to exclude fields that have a value of None
.
round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
"error" raises a [PydanticSerializationError
][pydantic_core.PydanticSerializationError].
serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.
Returns: A dictionary representation of the model.
Literal['json', 'python'] | str
- python
IncEx
IncEx
dict[str, Any] | None
bool
- False
bool
- False
bool
- False
bool
- False
bool
- False
bool | Literal['none', 'warn', 'error']
- True
bool
- False
dict[str, Any]
Usage docs: https://docs.pydantic.dev/2.7/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic's to_json
method.
Args:
indent: Indentation to use in the JSON output. If None is passed, the output will be compact.
include: Field(s) to include in the JSON output.
exclude: Field(s) to exclude from the JSON output.
context: Additional context to pass to the serializer.
by_alias: Whether to serialize using field aliases.
exclude_unset: Whether to exclude fields that have not been explicitly set.
exclude_defaults: Whether to exclude fields that are set to their default value.
exclude_none: Whether to exclude fields that have a value of None
.
round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
"error" raises a [PydanticSerializationError
][pydantic_core.PydanticSerializationError].
serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.
Returns: A JSON string representation of the model.
int | None
IncEx
IncEx
dict[str, Any] | None
bool
- False
bool
- False
bool
- False
bool
- False
bool
- False
bool | Literal['none', 'warn', 'error']
- True
bool
- False
str
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.
Any
Convert a ModelDTO to a json dictionary. This dict can be serialized.
dict
Convert a ModelDTO to a json string.
str
set[str] | None
Any
Model
Create a ModelDTO from a json.
dict
BaseModelDTOType
Create a list of ModelDTO from a list of json.
list
List
Create a ModelDTO from a string json.
str
BaseModelDTOType
Any
Model
Create a dict of ModelDTO from a dict of json.
dict
Dict
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.
Args: _fields_set: The set of field names accepted for the Model instance. values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the Model
class with validated data.
set[str] | None
Any
Model
Generates a JSON schema for a model class.
Args:
by_alias: Whether to use attribute aliases or not.
ref_template: The reference template.
schema_generator: To override the logic used to generate the JSON schema, as a subclass of
GenerateJsonSchema
with your desired modifications
mode: The mode in which to generate the schema.
Returns: The JSON schema for the given model class.
bool
- True
str
- #/$defs/{model}
type[GenerateJsonSchema]
- <class 'pydantic.json_schema.GenerateJsonSchema'>
JsonSchemaMode
- validation
dict[str, Any]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
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.
tuple[type[Any], ...]
str
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.
Args:
force: Whether to force the rebuilding of the model schema, defaults to False
.
raise_errors: Whether to raise errors, defaults to True
.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to None
.
Returns:
Returns None
if the schema is already "complete" and rebuilding was not required.
If rebuilding was required, returns True
if rebuilding was successful, otherwise False
.
bool
- False
bool
- True
int
- 2
dict[str, Any] | None
bool | None
Validate a pydantic model instance.
Args: obj: The object to validate. strict: Whether to enforce types strictly. from_attributes: Whether to extract data from object attributes. context: Additional context to pass to the validator.
Raises: ValidationError: If the object could not be validated.
Returns: The validated model instance.
Any
bool | None
bool | None
dict[str, Any] | None
Model
Usage docs: https://docs.pydantic.dev/2.7/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
Args: json_data: The JSON data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator.
Returns: The validated Pydantic model.
Raises:
ValueError: If json_data
is not a JSON string.
str | bytes | bytearray
bool | None
dict[str, Any] | None
Model
Validate the given object contains string data against the Pydantic model.
Args: obj: The object contains string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator.
Returns: The validated Pydantic model.
Any
bool | None
dict[str, Any] | None
Model
str | Path
str | None
str
- utf8
DeprecatedParseProtocol | None
bool
- False
Model
Any
Model
str | bytes
str | None
str
- utf8
DeprecatedParseProtocol | None
bool
- False
Model
bool
- True
str
- #/$defs/{model}
Dict[str, Any]
bool
- True
str
- #/$defs/{model}
Any
str
Any
Any
Model