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weave.trace_server.trace_server_interface

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Functions

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Field(default: 'Any' = PydanticUndefined, *, default_factory: 'Callable[[], Any] | Callable[[dict[str, Any]], Any] | None' = PydanticUndefined, alias: 'str | None' = PydanticUndefined, alias_priority: 'int | None' = PydanticUndefined, validation_alias: 'str | AliasPath | AliasChoices | None' = PydanticUndefined, serialization_alias: 'str | None' = PydanticUndefined, title: 'str | None' = PydanticUndefined, field_title_generator: 'Callable[[str, FieldInfo], str] | None' = PydanticUndefined, description: 'str | None' = PydanticUndefined, examples: 'list[Any] | None' = PydanticUndefined, exclude: 'bool | None' = PydanticUndefined, discriminator: 'str | types.Discriminator | None' = PydanticUndefined, deprecated: 'Deprecated | str | bool | None' = PydanticUndefined, json_schema_extra: 'JsonDict | Callable[[JsonDict], None] | None' = PydanticUndefined, frozen: 'bool | None' = PydanticUndefined, validate_default: 'bool | None' = PydanticUndefined, repr: 'bool' = PydanticUndefined, init: 'bool | None' = PydanticUndefined, init_var: 'bool | None' = PydanticUndefined, kw_only: 'bool | None' = PydanticUndefined, pattern: 'str | typing.Pattern[str] | None' = PydanticUndefined, strict: 'bool | None' = PydanticUndefined, coerce_numbers_to_str: 'bool | None' = PydanticUndefined, gt: 'annotated_types.SupportsGt | None' = PydanticUndefined, ge: 'annotated_types.SupportsGe | None' = PydanticUndefined, lt: 'annotated_types.SupportsLt | None' = PydanticUndefined, le: 'annotated_types.SupportsLe | None' = PydanticUndefined, multiple_of: 'float | None' = PydanticUndefined, allow_inf_nan: 'bool | None' = PydanticUndefined, max_digits: 'int | None' = PydanticUndefined, decimal_places: 'int | None' = PydanticUndefined, min_length: 'int | None' = PydanticUndefined, max_length: 'int | None' = PydanticUndefined, union_mode: "Literal['smart', 'left_to_right']" = PydanticUndefined, fail_fast: 'bool | None' = PydanticUndefined, **extra: 'Unpack[_EmptyKwargs]') -> 'Any'

!!! abstract “Usage Documentation” Fields Create a field for objects that can be configured. Used to provide extra information about a field, either for the model schema or complex validation. Some arguments apply only to number fields (int, float, Decimal) and some apply only to str. Note:
  • Any _Unset objects will be replaced by the corresponding value defined in the _DefaultValues dictionary. If a key for the _Unset object is not found in the _DefaultValues dictionary, it will default to None
Args: default: Default value if the field is not set. default_factory: A callable to generate the default value. The callable can either take 0 arguments (in which case it is called as is) or a single argument containing the already validated data. alias: The name to use for the attribute when validating or serializing by alias. This is often used for things like converting between snake and camel case. alias_priority: Priority of the alias. This affects whether an alias generator is used. validation_alias: Like alias, but only affects validation, not serialization. serialization_alias: Like alias, but only affects serialization, not validation. title: Human-readable title. field_title_generator: A callable that takes a field name and returns title for it. description: Human-readable description. examples: Example values for this field. exclude: Whether to exclude the field from the model serialization. discriminator: Field name or Discriminator for discriminating the type in a tagged union. deprecated: A deprecation message, an instance of warnings.deprecated or the typing_extensions.deprecated backport, or a boolean. If True, a default deprecation message will be emitted when accessing the field. json_schema_extra: A dict or callable to provide extra JSON schema properties. frozen: Whether the field is frozen. If true, attempts to change the value on an instance will raise an error. validate_default: If True, apply validation to the default value every time you create an instance. Otherwise, for performance reasons, the default value of the field is trusted and not validated. repr: A boolean indicating whether to include the field in the __repr__ output. init: Whether the field should be included in the constructor of the dataclass. (Only applies to dataclasses.) init_var: Whether the field should only be included in the constructor of the dataclass. (Only applies to dataclasses.) kw_only: Whether the field should be a keyword-only argument in the constructor of the dataclass. (Only applies to dataclasses.) coerce_numbers_to_str: Whether to enable coercion of any Number type to str (not applicable in strict mode). strict: If True, strict validation is applied to the field. See Strict Mode for details. gt: Greater than. If set, value must be greater than this. Only applicable to numbers. ge: Greater than or equal. If set, value must be greater than or equal to this. Only applicable to numbers. lt: Less than. If set, value must be less than this. Only applicable to numbers. le: Less than or equal. If set, value must be less than or equal to this. Only applicable to numbers. multiple_of: Value must be a multiple of this. Only applicable to numbers. min_length: Minimum length for iterables. max_length: Maximum length for iterables. pattern: Pattern for strings (a regular expression). allow_inf_nan: Allow inf, -inf, nan. Only applicable to float and [Decimal][decimal.Decimal] numbers. max_digits: Maximum number of allow digits for strings. decimal_places: Maximum number of decimal places allowed for numbers. union_mode: The strategy to apply when validating a union. Can be smart (the default), or left_to_right. See Union Mode for details. fail_fast: If True, validation will stop on the first error. If False, all validation errors will be collected. This option can be applied only to iterable types (list, tuple, set, and frozenset). extra: (Deprecated) Extra fields that will be included in the JSON schema. !!! warning Deprecated The extra kwargs is deprecated. Use json_schema_extra instead. Returns: A new [FieldInfo][pydantic.fields.FieldInfo]. The return annotation is Any so Field can be used on type-annotated fields without causing a type error.

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field_serializer(*fields: 'str', mode: "Literal['plain', 'wrap']" = 'plain', return_type: 'Any' = PydanticUndefined, when_used: 'WhenUsed' = 'always', check_fields: 'bool | None' = None) -> 'Callable[[_FieldWrapSerializerT], _FieldWrapSerializerT] | Callable[[_FieldPlainSerializerT], _FieldPlainSerializerT]'

Decorator that enables custom field serialization. In the below example, a field of type set is used to mitigate duplication. A field_serializer is used to serialize the data as a sorted list. 
from typing import Set

from pydantic import BaseModel, field_serializer

class StudentModel(BaseModel):
    name: str = 'Jane'
    courses: Set[str]

    @field_serializer('courses', when_used='json')
    def serialize_courses_in_order(self, courses: Set[str]):
        return sorted(courses)

student = StudentModel(courses={'Math', 'Chemistry', 'English'})
print(student.model_dump_json())
#> {"name":"Jane","courses":["Chemistry","English","Math"]}
See Custom serializers for more information. Four signatures are supported:
  • (self, value: Any, info: FieldSerializationInfo)
  • (self, value: Any, nxt: SerializerFunctionWrapHandler, info: FieldSerializationInfo)
  • (value: Any, info: SerializationInfo)
  • (value: Any, nxt: SerializerFunctionWrapHandler, info: SerializationInfo)
Args: fields: Which field(s) the method should be called on. mode: The serialization mode.
  • plain means the function will be called instead of the default serialization logic,
  • wrap means the function will be called with an argument to optionally call the default serialization logic. return_type: Optional return type for the function, if omitted it will be inferred from the type annotation. when_used: Determines the serializer will be used for serialization. check_fields: Whether to check that the fields actually exist on the model.
Returns: The decorator function.

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Classes

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ActionsExecuteBatchReq

Base model with strict validation that forbids extra fields.

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Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

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ActionsExecuteBatchRes

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

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Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
BaseModel

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
BaseModelStrict

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
CallBatchEndMode

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
CallBatchStartMode

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
CallCreateBatchReq

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
CallCreateBatchRes

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
CallEndReq

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
CallEndRes

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
CallReadReq

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
CallReadRes

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
CallSchema

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
serialize_typed_dicts(self, v: dict) -> dict
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
CallStartReq

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
CallStartRes

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
CallUpdateReq

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
CallUpdateRes

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
CallsDeleteReq

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
CallsDeleteRes

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
CallsFilter

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
CallsQueryReq

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
CallsQueryRes

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
CallsQueryStatsReq

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
CallsQueryStatsRes

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
CompletionsCreateReq

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
CompletionsCreateRequestInputs

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
CompletionsCreateRes

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
ConfigDict

A TypedDict for configuring Pydantic behaviour.

​
CostCreateInput

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
CostCreateReq

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
CostCreateRes

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
CostPurgeReq

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
CostPurgeRes

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
CostQueryOutput

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
CostQueryReq

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
CostQueryRes

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
EndedCallSchemaForInsert

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
serialize_typed_dicts(self, v: dict) -> dict
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
EnsureProjectExistsRes

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
Enum

Generic enumeration. Derive from this class to define new enumerations.

​
EvaluateModelReq

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
EvaluateModelRes

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
EvaluationStatusComplete

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
EvaluationStatusFailed

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
EvaluationStatusNotFound

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
EvaluationStatusReq

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
EvaluationStatusRes

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
EvaluationStatusRunning

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
ExportTracePartialSuccess

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
ExtraKeysTypedDict

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)

​
Feedback

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
FeedbackCreateBatchReq

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
FeedbackCreateBatchRes

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
FeedbackCreateReq

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
FeedbackCreateRes

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
FeedbackDict

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)

​
FeedbackPurgeReq

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
FeedbackPurgeRes

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
FeedbackQueryReq

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
FeedbackQueryRes

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
FeedbackReplaceReq

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
FeedbackReplaceRes

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
FileContentReadReq

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
FileContentReadRes

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
FileCreateReq

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
FileCreateRes

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
FilesStatsReq

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
FilesStatsRes

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
ImageGenerationCreateReq

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
ImageGenerationCreateRes

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
ImageGenerationRequestInputs

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
Iterator

No description available.

​
LLMCostSchema

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)

​
LLMUsageSchema

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)

​
ObjCreateReq

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
ObjCreateRes

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
ObjDeleteReq

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
ObjDeleteRes

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

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Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

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ObjQueryReq

Base model with strict validation that forbids extra fields.

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Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
ObjQueryRes

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
ObjReadReq

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
ObjReadRes

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
ObjSchema

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
ObjSchemaForInsert

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, _ObjSchemaForInsert__context: Any) -> None
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
ObjectVersionFilter

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
OpCreateReq

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
OpCreateRes

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
OpQueryReq

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
OpQueryRes

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
OpReadReq

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
OpReadRes

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
OpVersionFilter

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
OtelExportReq

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
OtelExportRes

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
ProjectStatsReq

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
ProjectStatsRes

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
Protocol

Base class for protocol classes. Protocol classes are defined as:: class Proto(Protocol): def meth(self) -> int: … Such classes are primarily used with static type checkers that recognize structural subtyping (static duck-typing), for example:: class C: def meth(self) -> int: return 0 def func(x: Proto) -> int: return x.meth() func(C()) # Passes static type check See PEP 544 for details. Protocol classes decorated with @typing.runtime_checkable act as simple-minded runtime protocols that check only the presence of given attributes, ignoring their type signatures. Protocol classes can be generic, they are defined as:: class GenProto(Protocol[T]): def meth(self) -> T: …

​
Query

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
RefsReadBatchReq

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
RefsReadBatchRes

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

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Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

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SortBy

Base model with strict validation that forbids extra fields.

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Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

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StartedCallSchemaForInsert

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
SummaryInsertMap

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)

​
SummaryMap

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)

​
TableAppendSpec

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
TableAppendSpecPayload

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
TableCreateFromDigestsReq

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
TableCreateFromDigestsRes

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
TableCreateReq

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
TableCreateRes

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
TableInsertSpec

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
TableInsertSpecPayload

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
TablePopSpec

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
TablePopSpecPayload

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
TableQueryReq

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
TableQueryRes

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
TableQueryStatsBatchReq

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
TableQueryStatsBatchRes

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
TableQueryStatsReq

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
TableQueryStatsRes

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
TableRowFilter

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
TableRowSchema

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
TableSchemaForInsert

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
TableStatsRow

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
TableUpdateReq

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
TableUpdateRes

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
ThreadSchema

!!! abstract “Usage Documentation” Models A base class for creating Pydantic models. Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model. pydantic_complete: Whether model building is completed, or if there are still undefined fields. pydantic_core_schema: The core schema of the model. pydantic_custom_init: Whether the model has a custom __init__ function. pydantic_decorators: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1. pydantic_generic_metadata: 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: Parent namespace of the model, used for automatic rebuilding of models. pydantic_post_init: The name of the post-init method for the model, if defined. pydantic_root_model: Whether the model is a [RootModel][pydantic.root_model.RootModel]. pydantic_serializer: The pydantic-core SchemaSerializer used to dump instances of the model. pydantic_validator: The pydantic-core SchemaValidator used to validate instances of the model. pydantic_fields: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. pydantic_computed_fields: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects. pydantic_extra: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'. pydantic_fields_set: The names of fields explicitly set during instantiation. pydantic_private: Values of private attributes set on the model instance.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
ThreadsQueryFilter

Base model with strict validation that forbids extra fields.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
ThreadsQueryReq

Query threads with aggregated statistics based on turn calls only. Turn calls are the immediate children of thread contexts (where call.id == turn_id). This provides meaningful conversation-level statistics rather than including all nested implementation details.

​
Methods

__init__(self, /, **data: 'Any') -> 'None'
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.
construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
No description available.
copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
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.
dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
No description available.
from_orm(obj: 'Any') -> 'Self'
No description available.
json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
No description available.
model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'
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: 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. values: Trusted or pre-validated data dictionary. Returns: A new instance of the Model class with validated data.
model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
!!! abstract “Usage Documentation” model_copy Returns a copy of the model. !!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]). 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.
model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A dictionary representation of the model.
model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
!!! abstract “Usage Documentation” model_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]. fallback: A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. Returns: A JSON string representation of the model.
model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'
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.
model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'
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.
model_post_init(self, context: 'Any', /) -> 'None'
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(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | 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. 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.
model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Raises: ValidationError: If the object could not be validated. Returns: The validated model instance.
model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
!!! abstract “Usage Documentation” 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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model. Raises: ValidationError: If json_data is not a JSON string or the object could not be validated.
model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self'
Validate the given object with string data against the Pydantic model. Args: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data. Returns: The validated Pydantic model.
parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
parse_obj(obj: 'Any') -> 'Self'
No description available.
parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'
No description available.
schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'
No description available.
schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'
No description available.
update_forward_refs(**localns: 'Any') -> 'None'
No description available.
validate(value: 'Any') -> 'Self'
No description available.

​
TraceServerInterface

Base class for protocol classes. Protocol classes are defined as:: class Proto(Protocol): def meth(self) -> int: … Such classes are primarily used with static type checkers that recognize structural subtyping (static duck-typing), for example:: class C: def meth(self) -> int: return 0 def func(x: Proto) -> int: return x.meth() func(C()) # Passes static type check See PEP 544 for details. Protocol classes decorated with @typing.runtime_checkable act as simple-minded runtime protocols that check only the presence of given attributes, ignoring their type signatures. Protocol classes can be generic, they are defined as:: class GenProto(Protocol[T]): def meth(self) -> T: …

​
Methods

_no_init(self, *args, **kwargs)
No description available.
actions_execute_batch(self, req: weave.trace_server.trace_server_interface.ActionsExecuteBatchReq) -> weave.trace_server.trace_server_interface.ActionsExecuteBatchRes
No description available.
call_end(self, req: weave.trace_server.trace_server_interface.CallEndReq) -> weave.trace_server.trace_server_interface.CallEndRes
No description available.
call_read(self, req: weave.trace_server.trace_server_interface.CallReadReq) -> weave.trace_server.trace_server_interface.CallReadRes
No description available.
call_start(self, req: weave.trace_server.trace_server_interface.CallStartReq) -> weave.trace_server.trace_server_interface.CallStartRes
No description available.
call_start_batch(self, req: weave.trace_server.trace_server_interface.CallCreateBatchReq) -> weave.trace_server.trace_server_interface.CallCreateBatchRes
No description available.
call_update(self, req: weave.trace_server.trace_server_interface.CallUpdateReq) -> weave.trace_server.trace_server_interface.CallUpdateRes
No description available.
calls_delete(self, req: weave.trace_server.trace_server_interface.CallsDeleteReq) -> weave.trace_server.trace_server_interface.CallsDeleteRes
No description available.
calls_query(self, req: weave.trace_server.trace_server_interface.CallsQueryReq) -> weave.trace_server.trace_server_interface.CallsQueryRes
No description available.
calls_query_stats(self, req: weave.trace_server.trace_server_interface.CallsQueryStatsReq) -> weave.trace_server.trace_server_interface.CallsQueryStatsRes
No description available.
calls_query_stream(self, req: weave.trace_server.trace_server_interface.CallsQueryReq) -> collections.abc.Iterator
No description available.
completions_create(self, req: weave.trace_server.trace_server_interface.CompletionsCreateReq) -> weave.trace_server.trace_server_interface.CompletionsCreateRes
No description available.
completions_create_stream(self, req: weave.trace_server.trace_server_interface.CompletionsCreateReq) -> collections.abc.Iterator
No description available.
cost_create(self, req: weave.trace_server.trace_server_interface.CostCreateReq) -> weave.trace_server.trace_server_interface.CostCreateRes
No description available.
cost_purge(self, req: weave.trace_server.trace_server_interface.CostPurgeReq) -> weave.trace_server.trace_server_interface.CostPurgeRes
No description available.
cost_query(self, req: weave.trace_server.trace_server_interface.CostQueryReq) -> weave.trace_server.trace_server_interface.CostQueryRes
No description available.
ensure_project_exists(self, entity: str, project: str) -> weave.trace_server.trace_server_interface.EnsureProjectExistsRes
No description available.
evaluate_model(self, req: weave.trace_server.trace_server_interface.EvaluateModelReq) -> weave.trace_server.trace_server_interface.EvaluateModelRes
No description available.
evaluation_status(self, req: weave.trace_server.trace_server_interface.EvaluationStatusReq) -> weave.trace_server.trace_server_interface.EvaluationStatusRes
No description available.
feedback_create(self, req: weave.trace_server.trace_server_interface.FeedbackCreateReq) -> weave.trace_server.trace_server_interface.FeedbackCreateRes
No description available.
feedback_create_batch(self, req: weave.trace_server.trace_server_interface.FeedbackCreateBatchReq) -> weave.trace_server.trace_server_interface.FeedbackCreateBatchRes
No description available.
feedback_purge(self, req: weave.trace_server.trace_server_interface.FeedbackPurgeReq) -> weave.trace_server.trace_server_interface.FeedbackPurgeRes
No description available.
feedback_query(self, req: weave.trace_server.trace_server_interface.FeedbackQueryReq) -> weave.trace_server.trace_server_interface.FeedbackQueryRes
No description available.
feedback_replace(self, req: weave.trace_server.trace_server_interface.FeedbackReplaceReq) -> weave.trace_server.trace_server_interface.FeedbackReplaceRes
No description available.
file_content_read(self, req: weave.trace_server.trace_server_interface.FileContentReadReq) -> weave.trace_server.trace_server_interface.FileContentReadRes
No description available.
file_create(self, req: weave.trace_server.trace_server_interface.FileCreateReq) -> weave.trace_server.trace_server_interface.FileCreateRes
No description available.
files_stats(self, req: weave.trace_server.trace_server_interface.FilesStatsReq) -> weave.trace_server.trace_server_interface.FilesStatsRes
No description available.
image_create(self, req: weave.trace_server.trace_server_interface.ImageGenerationCreateReq) -> weave.trace_server.trace_server_interface.ImageGenerationCreateRes
No description available.
obj_create(self, req: weave.trace_server.trace_server_interface.ObjCreateReq) -> weave.trace_server.trace_server_interface.ObjCreateRes
No description available.
obj_delete(self, req: weave.trace_server.trace_server_interface.ObjDeleteReq) -> weave.trace_server.trace_server_interface.ObjDeleteRes
No description available.
obj_read(self, req: weave.trace_server.trace_server_interface.ObjReadReq) -> weave.trace_server.trace_server_interface.ObjReadRes
No description available.
objs_query(self, req: weave.trace_server.trace_server_interface.ObjQueryReq) -> weave.trace_server.trace_server_interface.ObjQueryRes
No description available.
op_create(self, req: weave.trace_server.trace_server_interface.OpCreateReq) -> weave.trace_server.trace_server_interface.OpCreateRes
No description available.
op_read(self, req: weave.trace_server.trace_server_interface.OpReadReq) -> weave.trace_server.trace_server_interface.OpReadRes
No description available.
ops_query(self, req: weave.trace_server.trace_server_interface.OpQueryReq) -> weave.trace_server.trace_server_interface.OpQueryRes
No description available.
otel_export(self, req: weave.trace_server.trace_server_interface.OtelExportReq) -> weave.trace_server.trace_server_interface.OtelExportRes
No description available.
project_stats(self, req: weave.trace_server.trace_server_interface.ProjectStatsReq) -> weave.trace_server.trace_server_interface.ProjectStatsRes
No description available.
refs_read_batch(self, req: weave.trace_server.trace_server_interface.RefsReadBatchReq) -> weave.trace_server.trace_server_interface.RefsReadBatchRes
No description available.
table_create(self, req: weave.trace_server.trace_server_interface.TableCreateReq) -> weave.trace_server.trace_server_interface.TableCreateRes
No description available.
table_create_from_digests(self, req: weave.trace_server.trace_server_interface.TableCreateFromDigestsReq) -> weave.trace_server.trace_server_interface.TableCreateFromDigestsRes
No description available.
table_query(self, req: weave.trace_server.trace_server_interface.TableQueryReq) -> weave.trace_server.trace_server_interface.TableQueryRes
No description available.
table_query_stats(self, req: weave.trace_server.trace_server_interface.TableQueryStatsReq) -> weave.trace_server.trace_server_interface.TableQueryStatsRes
No description available.
table_query_stats_batch(self, req: weave.trace_server.trace_server_interface.TableQueryStatsBatchReq) -> weave.trace_server.trace_server_interface.TableQueryStatsBatchRes
No description available.
table_query_stream(self, req: weave.trace_server.trace_server_interface.TableQueryReq) -> collections.abc.Iterator
No description available.
table_update(self, req: weave.trace_server.trace_server_interface.TableUpdateReq) -> weave.trace_server.trace_server_interface.TableUpdateRes
No description available.
threads_query_stream(self, req: weave.trace_server.trace_server_interface.ThreadsQueryReq) -> collections.abc.Iterator
No description available.

​
TraceStatus

An enumeration.

​
WeaveSummarySchema

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)