BaseModel
Pydantic models are simply classes which inherit from BaseModel
and define fields as annotated attributes.
pydantic.BaseModel ¶
Usage Documentation
A base class for creating Pydantic models.
Attributes:
Name | Type | Description |
---|---|---|
__class_vars__ |
set[str]
|
The names of classvars defined on the model. |
__private_attributes__ |
dict[str, ModelPrivateAttr]
|
Metadata about the private attributes of the model. |
__signature__ |
Signature
|
The signature for instantiating the model. |
__pydantic_complete__ |
bool
|
Whether model building is completed, or if there are still undefined fields. |
__pydantic_core_schema__ |
CoreSchema
|
The pydantic-core schema used to build the SchemaValidator and SchemaSerializer. |
__pydantic_custom_init__ |
bool
|
Whether the model has a custom |
__pydantic_decorators__ |
_decorators.DecoratorInfos
|
Metadata containing the decorators defined on the model.
This replaces |
__pydantic_generic_metadata__ |
_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. |
__pydantic_parent_namespace__ |
dict[str, Any] | None
|
Parent namespace of the model, used for automatic rebuilding of models. |
__pydantic_post_init__ |
None | Literal['model_post_init']
|
The name of the post-init method for the model, if defined. |
__pydantic_root_model__ |
bool
|
Whether the model is a |
__pydantic_serializer__ |
SchemaSerializer
|
The pydantic-core SchemaSerializer used to dump instances of the model. |
__pydantic_validator__ |
SchemaValidator
|
The pydantic-core SchemaValidator used to validate instances of the model. |
__pydantic_extra__ |
dict[str, Any] | None
|
An instance attribute with the values of extra fields from validation when
|
__pydantic_fields_set__ |
set[str]
|
An instance attribute with the names of fields explicitly specified during validation. |
__pydantic_private__ |
dict[str, Any] | None
|
Instance attribute with the values of private attributes set on the model instance. |
Source code in pydantic/main.py
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|
__init__ ¶
__init__(__pydantic_self__, **data)
Raises ValidationError
if the input data cannot be
validated to form a valid model.
__init__
uses __pydantic_self__
instead of the more common self
for the first arg to
allow self
as a field name.
Source code in pydantic/main.py
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|
model_config
instance-attribute
class-attribute
¶
model_config: ConfigDict = ConfigDict()
Configuration for the model, should be a dictionary conforming to ConfigDict
.
model_computed_fields
property
¶
model_computed_fields: dict[str, ComputedFieldInfo]
Get the computed fields of this model instance.
Returns:
Type | Description |
---|---|
dict[str, ComputedFieldInfo]
|
A dictionary of computed field names and their corresponding |
model_extra
property
¶
model_extra: dict[str, Any] | None
Get extra fields set during validation.
Returns:
Type | Description |
---|---|
dict[str, Any] | None
|
A dictionary of extra fields, or |
model_fields_set
property
¶
model_fields_set: set[str]
Returns the set of fields that have been set on this model instance.
Returns:
Type | Description |
---|---|
set[str]
|
A set of strings representing the fields that have been set, i.e. that were not filled from defaults. |
model_construct
classmethod
¶
model_construct(_fields_set=None, **values)
Creates a new instance of the Model
class with validated data.
Creates a new model setting __dict__
and __pydantic_fields_set__
from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = 'allow'
was set since it adds all passed values
Parameters:
Name | Type | Description | Default |
---|---|---|---|
_fields_set |
set[str] | None
|
The set of field names accepted for the Model instance. |
None
|
values |
Any
|
Trusted or pre-validated data dictionary. |
{}
|
Returns:
Type | Description |
---|---|
Model
|
A new instance of the |
Source code in pydantic/main.py
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|
model_copy ¶
model_copy(*, update=None, deep=False)
Returns a copy of the model.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
update |
dict[str, Any] | None
|
Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data. |
None
|
deep |
bool
|
Set to |
False
|
Returns:
Type | Description |
---|---|
Model
|
New model instance. |
Source code in pydantic/main.py
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|
model_dump ¶
model_dump(
*,
mode="python",
include=None,
exclude=None,
by_alias=False,
exclude_unset=False,
exclude_defaults=False,
exclude_none=False,
round_trip=False,
warnings=True
)
Usage Documentation
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
mode |
Literal['json', 'python'] | str
|
The mode in which |
'python'
|
include |
IncEx
|
A list of fields to include in the output. |
None
|
exclude |
IncEx
|
A list of fields to exclude from the output. |
None
|
by_alias |
bool
|
Whether to use the field's alias in the dictionary key if defined. |
False
|
exclude_unset |
bool
|
Whether to exclude fields that are unset or None from the output. |
False
|
exclude_defaults |
bool
|
Whether to exclude fields that are set to their default value from the output. |
False
|
exclude_none |
bool
|
Whether to exclude fields that have a value of |
False
|
round_trip |
bool
|
Whether to enable serialization and deserialization round-trip support. |
False
|
warnings |
bool
|
Whether to log warnings when invalid fields are encountered. |
True
|
Returns:
Type | Description |
---|---|
dict[str, Any]
|
A dictionary representation of the model. |
Source code in pydantic/main.py
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model_dump_json ¶
model_dump_json(
*,
indent=None,
include=None,
exclude=None,
by_alias=False,
exclude_unset=False,
exclude_defaults=False,
exclude_none=False,
round_trip=False,
warnings=True
)
Usage Documentation
Generates a JSON representation of the model using Pydantic's to_json
method.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
indent |
int | None
|
Indentation to use in the JSON output. If None is passed, the output will be compact. |
None
|
include |
IncEx
|
Field(s) to include in the JSON output. Can take either a string or set of strings. |
None
|
exclude |
IncEx
|
Field(s) to exclude from the JSON output. Can take either a string or set of strings. |
None
|
by_alias |
bool
|
Whether to serialize using field aliases. |
False
|
exclude_unset |
bool
|
Whether to exclude fields that have not been explicitly set. |
False
|
exclude_defaults |
bool
|
Whether to exclude fields that have the default value. |
False
|
exclude_none |
bool
|
Whether to exclude fields that have a value of |
False
|
round_trip |
bool
|
Whether to use serialization/deserialization between JSON and class instance. |
False
|
warnings |
bool
|
Whether to show any warnings that occurred during serialization. |
True
|
Returns:
Type | Description |
---|---|
str
|
A JSON string representation of the model. |
Source code in pydantic/main.py
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|
model_json_schema
classmethod
¶
model_json_schema(
by_alias=True,
ref_template=DEFAULT_REF_TEMPLATE,
schema_generator=GenerateJsonSchema,
mode="validation",
)
Generates a JSON schema for a model class.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
by_alias |
bool
|
Whether to use attribute aliases or not. |
True
|
ref_template |
str
|
The reference template. |
DEFAULT_REF_TEMPLATE
|
schema_generator |
type[GenerateJsonSchema]
|
To override the logic used to generate the JSON schema, ass a subclass of
|
GenerateJsonSchema
|
mode |
JsonSchemaMode
|
The mode in which to generate the schema. |
'validation'
|
Returns:
Type | Description |
---|---|
dict[str, Any]
|
The JSON schema for the given model class. |
Source code in pydantic/main.py
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|
model_parametrized_name
classmethod
¶
model_parametrized_name(params)
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
params |
tuple[type[Any], ...]
|
Tuple of types of the class. Given a generic class
|
required |
Returns:
Type | Description |
---|---|
str
|
String representing the new class where |
Raises:
Type | Description |
---|---|
TypeError
|
Raised when trying to generate concrete names for non-generic models. |
Source code in pydantic/main.py
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|
model_post_init ¶
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.
Source code in pydantic/main.py
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|
model_rebuild
classmethod
¶
model_rebuild(
*,
force=False,
raise_errors=True,
_parent_namespace_depth=2,
_types_namespace=None
)
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
force |
bool
|
Whether to force the rebuilding of the model schema, defaults to |
False
|
raise_errors |
bool
|
Whether to raise errors, defaults to |
True
|
_parent_namespace_depth |
int
|
The depth level of the parent namespace, defaults to 2. |
2
|
_types_namespace |
dict[str, Any] | None
|
The types namespace, defaults to |
None
|
Returns:
Type | Description |
---|---|
bool | None
|
Returns |
bool | None
|
If rebuilding was required, returns |
Source code in pydantic/main.py
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model_validate
classmethod
¶
model_validate(
obj, *, strict=None, from_attributes=None, context=None
)
Validate a pydantic model instance.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
obj |
Any
|
The object to validate. |
required |
strict |
bool | None
|
Whether to raise an exception on invalid fields. |
None
|
from_attributes |
bool | None
|
Whether to extract data from object attributes. |
None
|
context |
dict[str, Any] | None
|
Additional context to pass to the validator. |
None
|
Raises:
Type | Description |
---|---|
ValidationError
|
If the object could not be validated. |
Returns:
Type | Description |
---|---|
Model
|
The validated model instance. |
Source code in pydantic/main.py
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|
model_validate_json
classmethod
¶
model_validate_json(
json_data, *, strict=None, context=None
)
Validate the given JSON data against the Pydantic model.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
json_data |
str | bytes | bytearray
|
The JSON data to validate. |
required |
strict |
bool | None
|
Whether to enforce types strictly. |
None
|
context |
dict[str, Any] | None
|
Extra variables to pass to the validator. |
None
|
Returns:
Type | Description |
---|---|
Model
|
The validated Pydantic model. |
Raises:
Type | Description |
---|---|
ValueError
|
If |
Source code in pydantic/main.py
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|
copy ¶
copy(
*, include=None, exclude=None, update=None, deep=False
)
Returns a copy of the model.
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)
Parameters:
Name | Type | Description | Default |
---|---|---|---|
include |
AbstractSetIntStr | MappingIntStrAny | None
|
Optional set or mapping specifying which fields to include in the copied model. |
None
|
exclude |
AbstractSetIntStr | MappingIntStrAny | None
|
Optional set or mapping specifying which fields to exclude in the copied model. |
None
|
update |
typing.Dict[str, Any] | None
|
Optional dictionary of field-value pairs to override field values in the copied model. |
None
|
deep |
bool
|
If True, the values of fields that are Pydantic models will be deep copied. |
False
|
Returns:
Type | Description |
---|---|
Model
|
A copy of the model with included, excluded and updated fields as specified. |
Source code in pydantic/main.py
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pydantic.create_model ¶
pydantic.create_model(
__model_name,
*,
__config__=None,
__base__=None,
__module__=__name__,
__validators__=None,
__cls_kwargs__=None,
__slots__=None,
**field_definitions
)
Dynamically creates and returns a new Pydantic model, in other words, create_model
dynamically creates a
subclass of BaseModel
.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
__model_name |
str
|
The name of the newly created model. |
required |
__config__ |
ConfigDict | None
|
The configuration of the new model. |
None
|
__base__ |
type[Model] | tuple[type[Model], ...] | None
|
The base class for the new model. |
None
|
__module__ |
str
|
The name of the module that the model belongs to. |
__name__
|
__validators__ |
dict[str, AnyClassMethod] | None
|
A dictionary of methods that validate fields. |
None
|
__cls_kwargs__ |
dict[str, Any] | None
|
A dictionary of keyword arguments for class creation. |
None
|
__slots__ |
tuple[str, ...] | None
|
Deprecated. Should not be passed to |
None
|
**field_definitions |
Any
|
Attributes of the new model. They should be passed in the format:
|
{}
|
Returns:
Type | Description |
---|---|
type[Model]
|
The new model. |
Raises:
Type | Description |
---|---|
PydanticUserError
|
If |
Source code in pydantic/main.py
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