AvroBase

fastkafka.encoder.AvroBase

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This is base pydantic class that will add some methods

init

__init__(
    __pydantic_self__, data
)

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

Uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

avro_schema

View source

@classmethod
avro_schema(
    by_alias=True, namespace=None
)

Returns the Avro schema for the Pydantic class.

Parameters:

Name	Type	Description	Default
by_alias	bool	Generate schemas using aliases defined. Defaults to True.	True
namespace	Optional[str]	Optional namespace string for schema generation.	None

Returns:

Type	Description
Dict[str, Any]	The Avro schema for the model.

avro_schema_for_pydantic_class

View source

@classmethod
avro_schema_for_pydantic_class(
    pydantic_model, by_alias=True, namespace=None
)

Returns the Avro schema for the given Pydantic class.

Parameters:

Name	Type	Description	Default
pydantic_model	Type[pydantic.main.BaseModel]	The Pydantic class.	required
by_alias	bool	Generate schemas using aliases defined. Defaults to True.	True
namespace	Optional[str]	Optional namespace string for schema generation.	None

Returns:

Type	Description
Dict[str, Any]	The Avro schema for the model.

avro_schema_for_pydantic_object

View source

@classmethod
avro_schema_for_pydantic_object(
    pydantic_model, by_alias=True, namespace=None
)

Returns the Avro schema for the given Pydantic object.

Parameters:

Name	Type	Description	Default
pydantic_model	BaseModel	The Pydantic object.	required
by_alias	bool	Generate schemas using aliases defined. Defaults to True.	True
namespace	Optional[str]	Optional namespace string for schema generation.	None

Returns:

Type	Description
Dict[str, Any]	The Avro schema for the model.

copy

copy(
    self, include=None, exclude=None, update=None, deep=False
)

Returns a copy of the model.

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
exclude	AbstractSetIntStr	MappingIntStrAny	None
update	`Dict[str, Any]	None`	Optional dictionary of field-value pairs to override field valuesin the copied model.
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.

model_computed_fields

@property
model_computed_fields(
    self
)

Get the computed fields of this model instance.

Returns:

Type	Description
dict[str, ComputedFieldInfo]	A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

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.
values	Any	Trusted or pre-validated data dictionary.	required

Returns:

Type	Description
Model	A new instance of the Model class with validated data.

model_copy

model_copy(
    self, 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 validatedbefore creating the new model. You should trust this data.
deep	bool	Set to True to make a deep copy of the model.	False

Returns:

Type	Description
Model	New model instance.

model_dump

model_dump(
    self,
    mode='python',
    include=None,
    exclude=None,
    by_alias=False,
    exclude_unset=False,
    exclude_defaults=False,
    exclude_none=False,
    round_trip=False,
    warnings=True,
)

Usage docs: https://docs.pydantic.dev/dev-v2/usage/serialization/#modelmodel_dump

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 to_python should run.If mode is 'json', the dictionary will only contain JSON serializable types.If mode is 'python', the dictionary may contain any Python objects.
include	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 None from the output.	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.

model_dump_json

model_dump_json(
    self,
    indent=None,
    include=None,
    exclude=None,
    by_alias=False,
    exclude_unset=False,
    exclude_defaults=False,
    exclude_none=False,
    round_trip=False,
    warnings=True,
)

Usage docs: https://docs.pydantic.dev/dev-v2/usage/serialization/#modelmodel_dump_json

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.
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 None.	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.

model_extra

@property
model_extra(
    self
)

Get extra fields set during validation.

Returns:

Type	Description
`dict[str, Any]	None`

model_fields_set

@property
model_fields_set(
    self
)

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_json_schema

@classmethod
model_json_schema(
    by_alias=True,
    ref_template='#/$defs/{model}',
    schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>,
    mode='validation',
)

Generates a JSON schema for a model class.

To override the logic used to generate the JSON schema, you can create a subclass of GenerateJsonSchema with your desired modifications, then override this method on a custom base class and set the default value of schema_generator to be your subclass.

Parameters:

Name	Type	Description	Default
by_alias	bool	Whether to use attribute aliases or not.	True
ref_template	str	The reference template.	'#/$defs/{model}'
schema_generator	type[GenerateJsonSchema]	The JSON schema generator.	<class 'pydantic.json_schema.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.

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 classModel with 2 type variables and a concrete model Model[str, int],the value (str, int) would be passed to params.	required

Returns:

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

Exceptions:

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

model_post_init

model_post_init(
    self, _BaseModel__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.

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.	False
raise_errors	bool	Whether to raise errors, defaults to True.	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`

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.
from_attributes	bool	None	Whether to extract data from object attributes.
context	dict[str, Any]	None	Additional context to pass to the validator.

Returns:

Type	Description
Model	The validated model instance.

Exceptions:

Type	Description
ValidationError	If the object could not be validated.

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
strict	bool	None	Whether to enforce types strictly.
context	dict[str, Any]	None	Extra variables to pass to the validator.

Returns:

Type	Description
Model	The validated Pydantic model.

Exceptions:

Type	Description
ValueError	If json_data is not a JSON string.