pw.xpacks.llm.prompts

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

sourceReturns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

\python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) ``

• Parameters
  • 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.

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(**values)

sourceCreates 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.

• Parameters
  • _fields_set (set[str] | None) – 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 (Any) – Trusted or pre-validated data dictionary.
• Returns
  A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

sourceUsage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#model_copy

Returns a copy of the model.

• Parameters
  • update (Optional[Mapping[str, Any]]) – 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 (bool) – Set to True to make a deep copy of the model.
• Returns
  New model instance.

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

sourceUsage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

• Parameters
  • mode (Union[Literal['json', 'python'], str]) – 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 (Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
  • exclude (Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
  • context (Any | None) – Additional context to pass to the serializer.
  • by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
  • exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
  • exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
  • exclude_none (bool) – Whether to exclude fields that have a value of None.
  • round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
  • warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
  • serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.
• Returns
  A dictionary representation of the model.

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

sourceUsage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

• Parameters
  • indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.
  • include (Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
  • exclude (Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
  • context (Any | None) – Additional context to pass to the serializer.
  • by_alias (bool) – Whether to serialize using field aliases.
  • exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
  • exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
  • exclude_none (bool) – Whether to exclude fields that have a value of None.
  • round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
  • warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
  • serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.
• Returns
  A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

• Returns
  A dictionary of extra fields, or None if config.extra is not set to “allow”.

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

• Returns
  A set of strings representing the fields that have been set,

    i.e. that were not filled from defaults.
  

classmethod model_json_schema(by_alias=True, ref_template=DEFAULT_REF_TEMPLATE, schema_generator=GenerateJsonSchema, mode='validation')

sourceGenerates a JSON schema for a model class.

• Parameters
  • by_alias (bool) – Whether to use attribute aliases or not.
  • ref_template (str) – The reference template.
  • schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
  • mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.
• Returns
  The JSON schema for the given model class.

classmethod model_parametrized_name(params)

sourceCompute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

• Parameters
  params (tuple[type[Any], ...]) – 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()

sourceOverride 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.

classmethod model_rebuild(*, force=False, raise_errors=True, )

sourceTry to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

• Parameters
  • force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
  • raise_errors (bool) – Whether to raise errors, defaults to True.
  • _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
  • _types_namespace (Optional[Mapping[str, Any]]) – 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.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)

sourceValidate a pydantic model instance.

• Parameters
  • obj (Any) – The object to validate.
  • strict (bool | None) – Whether to enforce types strictly.
  • from_attributes (bool | None) – Whether to extract data from object attributes.
  • context (Any | None) – Additional context to pass to the validator.
• Raises
  ValidationError – If the object could not be validated.
• Returns
  The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)

sourceUsage docs: https://docs.pydantic.dev/2.10/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

• Parameters
  • json_data (str | bytes | bytearray) – The JSON data to validate.
  • strict (bool | None) – Whether to enforce types strictly.
  • context (Any | None) – Extra variables to pass to the validator.
• Returns
  The validated Pydantic model.
• Raises
  ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)

sourceValidate the given object with string data against the Pydantic model.

• Parameters
  • obj (Any) – The object containing string data to validate.
  • strict (bool | None) – Whether to enforce types strictly.
  • context (Any | None) – Extra variables to pass to the validator.
• Returns
  The validated Pydantic model.

Utility class to create prompt templates from callables or UDF.

as_udf may take kwargs to partially pre-fill the prompt template.

copy(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, update: Dict[str, Any] | None = None, deep: bool = False)Self

sourceReturns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

\python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) ``

• Parameters
  • 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.

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(**values)

sourceCreates 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.

• Parameters
  • _fields_set (set[str] | None) – 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 (Any) – Trusted or pre-validated data dictionary.
• Returns
  A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

sourceUsage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#model_copy

Returns a copy of the model.

• Parameters
  • update (Optional[Mapping[str, Any]]) – 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 (bool) – Set to True to make a deep copy of the model.
• Returns
  New model instance.

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

sourceUsage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

• Parameters
  • mode (Union[Literal['json', 'python'], str]) – 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 (Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
  • exclude (Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
  • context (Any | None) – Additional context to pass to the serializer.
  • by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
  • exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
  • exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
  • exclude_none (bool) – Whether to exclude fields that have a value of None.
  • round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
  • warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
  • serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.
• Returns
  A dictionary representation of the model.

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

sourceUsage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

• Parameters
  • indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.
  • include (Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
  • exclude (Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
  • context (Any | None) – Additional context to pass to the serializer.
  • by_alias (bool) – Whether to serialize using field aliases.
  • exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
  • exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
  • exclude_none (bool) – Whether to exclude fields that have a value of None.
  • round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
  • warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
  • serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.
• Returns
  A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

• Returns
  A dictionary of extra fields, or None if config.extra is not set to “allow”.

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

• Returns
  A set of strings representing the fields that have been set,

    i.e. that were not filled from defaults.
  

classmethod model_json_schema(by_alias=True, ref_template=DEFAULT_REF_TEMPLATE, schema_generator=GenerateJsonSchema, mode='validation')

sourceGenerates a JSON schema for a model class.

• Parameters
  • by_alias (bool) – Whether to use attribute aliases or not.
  • ref_template (str) – The reference template.
  • schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
  • mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.
• Returns
  The JSON schema for the given model class.

classmethod model_parametrized_name(params)

sourceCompute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

• Parameters
  params (tuple[type[Any], ...]) – 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()

sourceOverride 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.

classmethod model_rebuild(*, force=False, raise_errors=True, )

sourceTry to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

• Parameters
  • force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
  • raise_errors (bool) – Whether to raise errors, defaults to True.
  • _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
  • _types_namespace (Optional[Mapping[str, Any]]) – 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.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)

sourceValidate a pydantic model instance.

• Parameters
  • obj (Any) – The object to validate.
  • strict (bool | None) – Whether to enforce types strictly.
  • from_attributes (bool | None) – Whether to extract data from object attributes.
  • context (Any | None) – Additional context to pass to the validator.
• Raises
  ValidationError – If the object could not be validated.
• Returns
  The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)

sourceUsage docs: https://docs.pydantic.dev/2.10/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

• Parameters
  • json_data (str | bytes | bytearray) – The JSON data to validate.
  • strict (bool | None) – Whether to enforce types strictly.
  • context (Any | None) – Extra variables to pass to the validator.
• Returns
  The validated Pydantic model.
• Raises
  ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)

sourceValidate the given object with string data against the Pydantic model.

• Parameters
  • obj (Any) – The object containing string data to validate.
  • strict (bool | None) – Whether to enforce types strictly.
  • context (Any | None) – Extra variables to pass to the validator.
• Returns
  The validated Pydantic model.

copy(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, update: Dict[str, Any] | None = None, deep: bool = False)Self

sourceReturns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

\python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) ``

• Parameters
  • 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.

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(**values)

sourceCreates 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.

• Parameters
  • _fields_set (set[str] | None) – 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 (Any) – Trusted or pre-validated data dictionary.
• Returns
  A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

sourceUsage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#model_copy

Returns a copy of the model.

• Parameters
  • update (Optional[Mapping[str, Any]]) – 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 (bool) – Set to True to make a deep copy of the model.
• Returns
  New model instance.

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

sourceUsage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

• Parameters
  • mode (Union[Literal['json', 'python'], str]) – 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 (Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
  • exclude (Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
  • context (Any | None) – Additional context to pass to the serializer.
  • by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
  • exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
  • exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
  • exclude_none (bool) – Whether to exclude fields that have a value of None.
  • round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
  • warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
  • serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.
• Returns
  A dictionary representation of the model.

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

sourceUsage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

• Parameters
  • indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.
  • include (Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
  • exclude (Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
  • context (Any | None) – Additional context to pass to the serializer.
  • by_alias (bool) – Whether to serialize using field aliases.
  • exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
  • exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
  • exclude_none (bool) – Whether to exclude fields that have a value of None.
  • round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
  • warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
  • serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.
• Returns
  A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

• Returns
  A dictionary of extra fields, or None if config.extra is not set to “allow”.

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

• Returns
  A set of strings representing the fields that have been set,

    i.e. that were not filled from defaults.
  

classmethod model_json_schema(by_alias=True, ref_template=DEFAULT_REF_TEMPLATE, schema_generator=GenerateJsonSchema, mode='validation')

sourceGenerates a JSON schema for a model class.

• Parameters
  • by_alias (bool) – Whether to use attribute aliases or not.
  • ref_template (str) – The reference template.
  • schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
  • mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.
• Returns
  The JSON schema for the given model class.

classmethod model_parametrized_name(params)

sourceCompute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

• Parameters
  params (tuple[type[Any], ...]) – 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()

sourceOverride 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.

classmethod model_rebuild(*, force=False, raise_errors=True, )

sourceTry to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

• Parameters
  • force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
  • raise_errors (bool) – Whether to raise errors, defaults to True.
  • _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
  • _types_namespace (Optional[Mapping[str, Any]]) – 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.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)

sourceValidate a pydantic model instance.

• Parameters
  • obj (Any) – The object to validate.
  • strict (bool | None) – Whether to enforce types strictly.
  • from_attributes (bool | None) – Whether to extract data from object attributes.
  • context (Any | None) – Additional context to pass to the validator.
• Raises
  ValidationError – If the object could not be validated.
• Returns
  The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)

sourceUsage docs: https://docs.pydantic.dev/2.10/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

• Parameters
  • json_data (str | bytes | bytearray) – The JSON data to validate.
  • strict (bool | None) – Whether to enforce types strictly.
  • context (Any | None) – Extra variables to pass to the validator.
• Returns
  The validated Pydantic model.
• Raises
  ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)

sourceValidate the given object with string data against the Pydantic model.

• Parameters
  • obj (Any) – The object containing string data to validate.
  • strict (bool | None) – Whether to enforce types strictly.
  • context (Any | None) – Extra variables to pass to the validator.
• Returns
  The validated Pydantic model.

copy(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, update: Dict[str, Any] | None = None, deep: bool = False)Self

sourceReturns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

\python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) ``

• Parameters
  • 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.

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(**values)

sourceCreates 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.

• Parameters
  • _fields_set (set[str] | None) – 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 (Any) – Trusted or pre-validated data dictionary.
• Returns
  A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

sourceUsage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#model_copy

Returns a copy of the model.

• Parameters
  • update (Optional[Mapping[str, Any]]) – 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 (bool) – Set to True to make a deep copy of the model.
• Returns
  New model instance.

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

sourceUsage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

• Parameters
  • mode (Union[Literal['json', 'python'], str]) – 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 (Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
  • exclude (Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
  • context (Any | None) – Additional context to pass to the serializer.
  • by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
  • exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
  • exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
  • exclude_none (bool) – Whether to exclude fields that have a value of None.
  • round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
  • warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
  • serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.
• Returns
  A dictionary representation of the model.

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

sourceUsage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

• Parameters
  • indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.
  • include (Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
  • exclude (Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
  • context (Any | None) – Additional context to pass to the serializer.
  • by_alias (bool) – Whether to serialize using field aliases.
  • exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
  • exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
  • exclude_none (bool) – Whether to exclude fields that have a value of None.
  • round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
  • warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
  • serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.
• Returns
  A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

• Returns
  A dictionary of extra fields, or None if config.extra is not set to “allow”.

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

• Returns
  A set of strings representing the fields that have been set,

    i.e. that were not filled from defaults.
  

classmethod model_json_schema(by_alias=True, ref_template=DEFAULT_REF_TEMPLATE, schema_generator=GenerateJsonSchema, mode='validation')

sourceGenerates a JSON schema for a model class.

• Parameters
  • by_alias (bool) – Whether to use attribute aliases or not.
  • ref_template (str) – The reference template.
  • schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
  • mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.
• Returns
  The JSON schema for the given model class.

classmethod model_parametrized_name(params)

sourceCompute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

• Parameters
  params (tuple[type[Any], ...]) – 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()

sourceOverride 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.

classmethod model_rebuild(*, force=False, raise_errors=True, )

sourceTry to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

• Parameters
  • force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
  • raise_errors (bool) – Whether to raise errors, defaults to True.
  • _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
  • _types_namespace (Optional[Mapping[str, Any]]) – 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.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)

sourceValidate a pydantic model instance.

• Parameters
  • obj (Any) – The object to validate.
  • strict (bool | None) – Whether to enforce types strictly.
  • from_attributes (bool | None) – Whether to extract data from object attributes.
  • context (Any | None) – Additional context to pass to the validator.
• Raises
  ValidationError – If the object could not be validated.
• Returns
  The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)

sourceUsage docs: https://docs.pydantic.dev/2.10/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

• Parameters
  • json_data (str | bytes | bytearray) – The JSON data to validate.
  • strict (bool | None) – Whether to enforce types strictly.
  • context (Any | None) – Extra variables to pass to the validator.
• Returns
  The validated Pydantic model.
• Raises
  ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)

sourceValidate the given object with string data against the Pydantic model.

• Parameters
  • obj (Any) – The object containing string data to validate.
  • strict (bool | None) – Whether to enforce types strictly.
  • context (Any | None) – Extra variables to pass to the validator.
• Returns
  The validated Pydantic model.

Utility class to create prompt templates that can be applied to tables.

import pandas as pd
import pathway as pw
prompt_template = "Answer the following question. Context: {context}. Question: {query}"
t = pw.debug.table_from_pandas(pd.DataFrame([{"context": "Here are some facts...",
    "query": "How much do penguins weigh in average?"}]))
template = StringPromptTemplate(template=prompt_template)
template_udf = template.as_udf()
t = t.select(prompt=template_udf(context=pw.this.context, query=pw.this.query))

copy(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, update: Dict[str, Any] | None = None, deep: bool = False)Self

sourceReturns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

\python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) ``

• Parameters
  • 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.

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(**values)

sourceCreates 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.

• Parameters
  • _fields_set (set[str] | None) – 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 (Any) – Trusted or pre-validated data dictionary.
• Returns
  A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

sourceUsage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#model_copy

Returns a copy of the model.

• Parameters
  • update (Optional[Mapping[str, Any]]) – 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 (bool) – Set to True to make a deep copy of the model.
• Returns
  New model instance.

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

sourceUsage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

• Parameters
  • mode (Union[Literal['json', 'python'], str]) – 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 (Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
  • exclude (Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
  • context (Any | None) – Additional context to pass to the serializer.
  • by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
  • exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
  • exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
  • exclude_none (bool) – Whether to exclude fields that have a value of None.
  • round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
  • warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
  • serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.
• Returns
  A dictionary representation of the model.

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

sourceUsage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

• Parameters
  • indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.
  • include (Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
  • exclude (Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
  • context (Any | None) – Additional context to pass to the serializer.
  • by_alias (bool) – Whether to serialize using field aliases.
  • exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
  • exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
  • exclude_none (bool) – Whether to exclude fields that have a value of None.
  • round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
  • warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
  • serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.
• Returns
  A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

• Returns
  A dictionary of extra fields, or None if config.extra is not set to “allow”.

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

• Returns
  A set of strings representing the fields that have been set,

    i.e. that were not filled from defaults.
  

classmethod model_json_schema(by_alias=True, ref_template=DEFAULT_REF_TEMPLATE, schema_generator=GenerateJsonSchema, mode='validation')

sourceGenerates a JSON schema for a model class.

• Parameters
  • by_alias (bool) – Whether to use attribute aliases or not.
  • ref_template (str) – The reference template.
  • schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
  • mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.
• Returns
  The JSON schema for the given model class.

classmethod model_parametrized_name(params)

sourceCompute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

• Parameters
  params (tuple[type[Any], ...]) – 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()

sourceOverride 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.

classmethod model_rebuild(*, force=False, raise_errors=True, )

sourceTry to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

• Parameters
  • force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
  • raise_errors (bool) – Whether to raise errors, defaults to True.
  • _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
  • _types_namespace (Optional[Mapping[str, Any]]) – 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.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)

sourceValidate a pydantic model instance.

• Parameters
  • obj (Any) – The object to validate.
  • strict (bool | None) – Whether to enforce types strictly.
  • from_attributes (bool | None) – Whether to extract data from object attributes.
  • context (Any | None) – Additional context to pass to the validator.
• Raises
  ValidationError – If the object could not be validated.
• Returns
  The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)

sourceUsage docs: https://docs.pydantic.dev/2.10/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

• Parameters
  • json_data (str | bytes | bytearray) – The JSON data to validate.
  • strict (bool | None) – Whether to enforce types strictly.
  • context (Any | None) – Extra variables to pass to the validator.
• Returns
  The validated Pydantic model.
• Raises
  ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)

sourceValidate the given object with string data against the Pydantic model.

• Parameters
  • obj (Any) – The object containing string data to validate.
  • strict (bool | None) – Whether to enforce types strictly.
  • context (Any | None) – Extra variables to pass to the validator.
• Returns
  The validated Pydantic model.