Pipeline API

Experimental pipeline API functionality. Be careful with this API, it's subject to change.

_Pipeline dataclass

_Pipeline(_steps: tuple[_Step, ...])

Bases: Generic[_InT, _OutT]

Abstract representation of a chain of validation, transformation, and parsing steps.

transform

transform(
    func: Callable[[_OutT], _NewOutT]
) -> _Pipeline[_InT, _NewOutT]

Transform the output of the previous step.

If used as the first step in a pipeline, the type of the field is used. That is, the transformation is applied to after the value is parsed to the field's type.

Source code in pydantic/experimental/pipeline.py

def transform(
    self,
    func: Callable[[_OutT], _NewOutT],
) -> _Pipeline[_InT, _NewOutT]:
    """Transform the output of the previous step.

    If used as the first step in a pipeline, the type of the field is used.
    That is, the transformation is applied to after the value is parsed to the field's type.
    """
    return _Pipeline[_InT, _NewOutT](self._steps + (_Transform(func),))

validate_as

validate_as(
    tp: type[_NewOutT], *, strict: bool = ...
) -> _Pipeline[_InT, _NewOutT]

validate_as(
    tp: EllipsisType, *, strict: bool = ...
) -> _Pipeline[_InT, Any]

validate_as(
    tp: type[_NewOutT] | EllipsisType,
    *,
    strict: bool = False
) -> _Pipeline[_InT, Any]

Validate / parse the input into a new type.

If no type is provided, the type of the field is used.

Types are parsed in Pydantic's lax mode by default, but you can enable strict mode by passing strict=True.

Source code in pydantic/experimental/pipeline.py

def validate_as(self, tp: type[_NewOutT] | EllipsisType, *, strict: bool = False) -> _Pipeline[_InT, Any]:  # type: ignore
    """Validate / parse the input into a new type.

    If no type is provided, the type of the field is used.

    Types are parsed in Pydantic's `lax` mode by default,
    but you can enable `strict` mode by passing `strict=True`.
    """
    if isinstance(tp, EllipsisType):
        return _Pipeline[_InT, Any](self._steps + (_ValidateAs(_FieldTypeMarker, strict=strict),))
    return _Pipeline[_InT, _NewOutT](self._steps + (_ValidateAs(tp, strict=strict),))

validate_as_deferred

validate_as_deferred(
    func: Callable[[], type[_NewOutT]]
) -> _Pipeline[_InT, _NewOutT]

Parse the input into a new type, deferring resolution of the type until the current class is fully defined.

This is useful when you need to reference the class in it's own type annotations.

Source code in pydantic/experimental/pipeline.py

def validate_as_deferred(self, func: Callable[[], type[_NewOutT]]) -> _Pipeline[_InT, _NewOutT]:
    """Parse the input into a new type, deferring resolution of the type until the current class
    is fully defined.

    This is useful when you need to reference the class in it's own type annotations.
    """
    return _Pipeline[_InT, _NewOutT](self._steps + (_ValidateAsDefer(func),))

constrain

constrain(constraint: Ge) -> _Pipeline[_InT, _NewOutGe]

constrain(constraint: Gt) -> _Pipeline[_InT, _NewOutGt]

constrain(constraint: Le) -> _Pipeline[_InT, _NewOutLe]

constrain(constraint: Lt) -> _Pipeline[_InT, _NewOutLt]

constrain(constraint: Len) -> _Pipeline[_InT, _NewOutLen]

constrain(
    constraint: MultipleOf,
) -> _Pipeline[_InT, _NewOutT]

constrain(
    constraint: Timezone,
) -> _Pipeline[_InT, _NewOutDatetime]

constrain(constraint: Predicate) -> _Pipeline[_InT, _OutT]

constrain(
    constraint: Interval,
) -> _Pipeline[_InT, _NewOutInterval]

constrain(constraint: _Eq) -> _Pipeline[_InT, _OutT]

constrain(constraint: _NotEq) -> _Pipeline[_InT, _OutT]

constrain(constraint: _In) -> _Pipeline[_InT, _OutT]

constrain(constraint: _NotIn) -> _Pipeline[_InT, _OutT]

constrain(
    constraint: Pattern[str],
) -> _Pipeline[_InT, _NewOutT]

constrain(constraint: _ConstraintAnnotation) -> Any

Constrain a value to meet a certain condition.

We support most conditions from annotated_types, as well as regular expressions.

Most of the time you'll be calling a shortcut method like gt, lt, len, etc so you don't need to call this directly.

Source code in pydantic/experimental/pipeline.py

def constrain(self, constraint: _ConstraintAnnotation) -> Any:
    """Constrain a value to meet a certain condition.

    We support most conditions from `annotated_types`, as well as regular expressions.

    Most of the time you'll be calling a shortcut method like `gt`, `lt`, `len`, etc
    so you don't need to call this directly.
    """
    return _Pipeline[_InT, _OutT](self._steps + (_Constraint(constraint),))

predicate

predicate(
    func: Callable[[_NewOutT], bool]
) -> _Pipeline[_InT, _NewOutT]

Constrain a value to meet a certain predicate.

Source code in pydantic/experimental/pipeline.py

def predicate(self: _Pipeline[_InT, _NewOutT], func: Callable[[_NewOutT], bool]) -> _Pipeline[_InT, _NewOutT]:
    """Constrain a value to meet a certain predicate."""
    return self.constrain(annotated_types.Predicate(func))

gt

gt(gt: _NewOutGt) -> _Pipeline[_InT, _NewOutGt]

Constrain a value to be greater than a certain value.

Source code in pydantic/experimental/pipeline.py

def gt(self: _Pipeline[_InT, _NewOutGt], gt: _NewOutGt) -> _Pipeline[_InT, _NewOutGt]:
    """Constrain a value to be greater than a certain value."""
    return self.constrain(annotated_types.Gt(gt))

lt

lt(lt: _NewOutLt) -> _Pipeline[_InT, _NewOutLt]

Constrain a value to be less than a certain value.

Source code in pydantic/experimental/pipeline.py

def lt(self: _Pipeline[_InT, _NewOutLt], lt: _NewOutLt) -> _Pipeline[_InT, _NewOutLt]:
    """Constrain a value to be less than a certain value."""
    return self.constrain(annotated_types.Lt(lt))

ge

ge(ge: _NewOutGe) -> _Pipeline[_InT, _NewOutGe]

Constrain a value to be greater than or equal to a certain value.

Source code in pydantic/experimental/pipeline.py

def ge(self: _Pipeline[_InT, _NewOutGe], ge: _NewOutGe) -> _Pipeline[_InT, _NewOutGe]:
    """Constrain a value to be greater than or equal to a certain value."""
    return self.constrain(annotated_types.Ge(ge))

le

le(le: _NewOutLe) -> _Pipeline[_InT, _NewOutLe]

Constrain a value to be less than or equal to a certain value.

Source code in pydantic/experimental/pipeline.py

def le(self: _Pipeline[_InT, _NewOutLe], le: _NewOutLe) -> _Pipeline[_InT, _NewOutLe]:
    """Constrain a value to be less than or equal to a certain value."""
    return self.constrain(annotated_types.Le(le))

len

len(
    min_len: int, max_len: int | None = None
) -> _Pipeline[_InT, _NewOutLen]

Constrain a value to have a certain length.

Source code in pydantic/experimental/pipeline.py

def len(self: _Pipeline[_InT, _NewOutLen], min_len: int, max_len: int | None = None) -> _Pipeline[_InT, _NewOutLen]:
    """Constrain a value to have a certain length."""
    return self.constrain(annotated_types.Len(min_len, max_len))

multiple_of

multiple_of(
    multiple_of: _NewOutDiv,
) -> _Pipeline[_InT, _NewOutDiv]

multiple_of(
    multiple_of: _NewOutMod,
) -> _Pipeline[_InT, _NewOutMod]

multiple_of(multiple_of: Any) -> _Pipeline[_InT, Any]

Constrain a value to be a multiple of a certain number.

Source code in pydantic/experimental/pipeline.py

def multiple_of(self: _Pipeline[_InT, Any], multiple_of: Any) -> _Pipeline[_InT, Any]:
    """Constrain a value to be a multiple of a certain number."""
    return self.constrain(annotated_types.MultipleOf(multiple_of))

eq

eq(value: _OutT) -> _Pipeline[_InT, _OutT]

Constrain a value to be equal to a certain value.

Source code in pydantic/experimental/pipeline.py

def eq(self: _Pipeline[_InT, _OutT], value: _OutT) -> _Pipeline[_InT, _OutT]:
    """Constrain a value to be equal to a certain value."""
    return self.constrain(_Eq(value))

not_eq

not_eq(value: _OutT) -> _Pipeline[_InT, _OutT]

Constrain a value to not be equal to a certain value.

Source code in pydantic/experimental/pipeline.py

def not_eq(self: _Pipeline[_InT, _OutT], value: _OutT) -> _Pipeline[_InT, _OutT]:
    """Constrain a value to not be equal to a certain value."""
    return self.constrain(_NotEq(value))

in_

in_(values: Container[_OutT]) -> _Pipeline[_InT, _OutT]

Constrain a value to be in a certain set.

Source code in pydantic/experimental/pipeline.py

def in_(self: _Pipeline[_InT, _OutT], values: Container[_OutT]) -> _Pipeline[_InT, _OutT]:
    """Constrain a value to be in a certain set."""
    return self.constrain(_In(values))

not_in

not_in(values: Container[_OutT]) -> _Pipeline[_InT, _OutT]

Constrain a value to not be in a certain set.

Source code in pydantic/experimental/pipeline.py

def not_in(self: _Pipeline[_InT, _OutT], values: Container[_OutT]) -> _Pipeline[_InT, _OutT]:
    """Constrain a value to not be in a certain set."""
    return self.constrain(_NotIn(values))

otherwise

otherwise(
    other: _Pipeline[_OtherIn, _OtherOut]
) -> _Pipeline[_InT | _OtherIn, _OutT | _OtherOut]

Combine two validation chains, returning the result of the first chain if it succeeds, and the second chain if it fails.

Source code in pydantic/experimental/pipeline.py

def otherwise(self, other: _Pipeline[_OtherIn, _OtherOut]) -> _Pipeline[_InT | _OtherIn, _OutT | _OtherOut]:
    """Combine two validation chains, returning the result of the first chain if it succeeds, and the second chain if it fails."""
    return _Pipeline((_PipelineOr(self, other),))

then

then(
    other: _Pipeline[_OutT, _OtherOut]
) -> _Pipeline[_InT, _OtherOut]

Pipe the result of one validation chain into another.

Source code in pydantic/experimental/pipeline.py

def then(self, other: _Pipeline[_OutT, _OtherOut]) -> _Pipeline[_InT, _OtherOut]:
    """Pipe the result of one validation chain into another."""
    return _Pipeline((_PipelineAnd(self, other),))

Arguments schema API

Experimental module exposing a function to generate a core schema that validates callable arguments.

generate_arguments_schema

generate_arguments_schema(
    func: Callable[..., Any],
    schema_type: Literal[
        "arguments", "arguments-v3"
    ] = "arguments-v3",
    parameters_callback: (
        Callable[[int, str, Any], Literal["skip"] | None]
        | None
    ) = None,
    config: ConfigDict | None = None,
) -> CoreSchema

Generate the schema for the arguments of a function.

Parameters:

Name	Type	Description	Default
func	Callable[..., Any]	The function to generate the schema for.	required
schema_type	Literal['arguments', 'arguments-v3']	The type of schema to generate.	'arguments-v3'
parameters_callback	Callable[[int, str, Any], Literal['skip'] | None] | None	A callable that will be invoked for each parameter. The callback should take three required arguments: the index, the name and the type annotation (or Parameter.empty if not annotated) of the parameter. The callback can optionally return 'skip', so that the parameter gets excluded from the resulting schema.	None
config	ConfigDict | None	The configuration to use.	None

Returns:

Type	Description
CoreSchema	The generated schema.

Source code in pydantic/experimental/arguments_schema.py

def generate_arguments_schema(
    func: Callable[..., Any],
    schema_type: Literal['arguments', 'arguments-v3'] = 'arguments-v3',
    parameters_callback: Callable[[int, str, Any], Literal['skip'] | None] | None = None,
    config: ConfigDict | None = None,
) -> CoreSchema:
    """Generate the schema for the arguments of a function.

    Args:
        func: The function to generate the schema for.
        schema_type: The type of schema to generate.
        parameters_callback: A callable that will be invoked for each parameter. The callback
            should take three required arguments: the index, the name and the type annotation
            (or [`Parameter.empty`][inspect.Parameter.empty] if not annotated) of the parameter.
            The callback can optionally return `'skip'`, so that the parameter gets excluded
            from the resulting schema.
        config: The configuration to use.

    Returns:
        The generated schema.
    """
    generate_schema = _generate_schema.GenerateSchema(
        _config.ConfigWrapper(config),
        ns_resolver=_namespace_utils.NsResolver(namespaces_tuple=_namespace_utils.ns_for_function(func)),
    )

    if schema_type == 'arguments':
        schema = generate_schema._arguments_schema(func, parameters_callback)  # pyright: ignore[reportArgumentType]
    else:
        schema = generate_schema._arguments_v3_schema(func, parameters_callback)  # pyright: ignore[reportArgumentType]
    return generate_schema.clean_schema(schema)