flytekit.extras.pydantic_transformer.transformer

Directory

Classes

Variables

flytekit.extras.pydantic_transformer.transformer.PydanticTransformer

Parameters

def PydanticTransformer()

Properties

Methods

assert_type()

def assert_type(
    t: Type[T],
    v: T,
)

from_binary_idl()

def from_binary_idl(
    binary_idl_object: flytekit.models.literals.Binary,
    expected_python_type: typing.Type[pydantic.main.BaseModel],
) -> pydantic.main.BaseModel

This function primarily handles deserialization for untyped dicts, dataclasses, Pydantic BaseModels, and attribute access.｀

For untyped dict, dataclass, and pydantic basemodel: Life Cycle (Untyped Dict as example): python val -> msgpack bytes -> binary literal scalar -> msgpack bytes -> python val (to_literal) (from_binary_idl)

For attribute access: Life Cycle: python val -> msgpack bytes -> binary literal scalar -> resolved golang value -> binary literal scalar -> msgpack bytes -> python val (to_literal) (propeller attribute access) (from_binary_idl)

from_generic_idl()

def from_generic_idl(
    generic: Struct,
    expected_python_type: Type[T],
) -> Optional[T]

TODO: Support all Flyte Types. This is for dataclass attribute access from input created from the Flyte Console.

• This can be removed in the future when the Flyte Console support generate Binary IDL Scalar as input.

get_literal_type()

def get_literal_type(
    t: typing.Type[pydantic.main.BaseModel],
) -> flytekit.models.types.LiteralType

Converts the python type to a Flyte LiteralType

guess_python_type()

def guess_python_type(
    literal_type: LiteralType,
) -> Type[T]

Converts the Flyte LiteralType to a python object type.

isinstance_generic()

def isinstance_generic(
    obj,
    generic_alias,
)

schema_match()

def schema_match(
    schema: dict,
) -> bool

Check if a JSON schema fragment matches this transformer’s python_type.

For BaseModel subclasses, automatically compares the schema’s title, type, and required fields against the type’s own JSON schema. For other types, returns False by default — override if needed.

to_generic_literal()

def to_generic_literal(
    ctx: flytekit.core.context_manager.FlyteContext,
    python_val: pydantic.main.BaseModel,
    python_type: typing.Type[pydantic.main.BaseModel],
    expected: flytekit.models.types.LiteralType,
) -> flytekit.models.literals.Literal

Note: This is deprecated and will be removed in the future.

to_html()

def to_html(
    ctx: FlyteContext,
    python_val: T,
    expected_python_type: Type[T],
) -> str

Converts any python val (dataframe, int, float) to a html string, and it will be wrapped in the HTML div

to_literal()

def to_literal(
    ctx: flytekit.core.context_manager.FlyteContext,
    python_val: pydantic.main.BaseModel,
    python_type: typing.Type[pydantic.main.BaseModel],
    expected: flytekit.models.types.LiteralType,
) -> flytekit.models.literals.Literal

For pydantic basemodel, we have to go through json first. This is for handling enum in basemodel. More details: https://github.com/flyteorg/flytekit/pull/2792

to_python_value()

def to_python_value(
    ctx: flytekit.core.context_manager.FlyteContext,
    lv: flytekit.models.literals.Literal,
    expected_python_type: typing.Type[pydantic.main.BaseModel],
) -> pydantic.main.BaseModel

There will have 2 kinds of literal values:

1. protobuf Struct (From Flyte Console)
2. binary scalar (Others) Hence we have to handle 2 kinds of cases.