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2c1ab4879f
refactor(api): Separate SegmentType for Integer/Float to Enable Pydantic Serialization (#22025) This PR addresses serialization issues in the VariablePool model by separating the `value_type` tags for `IntegerSegment`/`FloatSegment` and `IntegerVariable`/`FloatVariable`. Previously, both Integer and Float types shared the same `SegmentType.NUMBER` tag, causing conflicts during serialization. Key changes: - Introduce distinct `value_type` tags for Integer and Float segments/variables - Add `VariableUnion` and `SegmentUnion` types for proper type discrimination - Leverage Pydantic's discriminated union feature for seamless serialization/deserialization - Enable accurate serialization of data structures containing these types Closes #22024.
126 lines
3.2 KiB
Python
126 lines
3.2 KiB
Python
from collections.abc import Sequence
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from typing import Annotated, TypeAlias, cast
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from uuid import uuid4
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from pydantic import Discriminator, Field, Tag
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from core.helper import encrypter
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from .segments import (
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ArrayAnySegment,
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ArrayFileSegment,
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ArrayNumberSegment,
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ArrayObjectSegment,
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ArraySegment,
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ArrayStringSegment,
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FileSegment,
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FloatSegment,
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IntegerSegment,
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NoneSegment,
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ObjectSegment,
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Segment,
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StringSegment,
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get_segment_discriminator,
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)
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from .types import SegmentType
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class Variable(Segment):
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"""
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A variable is a segment that has a name.
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It is mainly used to store segments and their selector in VariablePool.
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Note: this class is abstract, you should use subclasses of this class instead.
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"""
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id: str = Field(
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default_factory=lambda: str(uuid4()),
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description="Unique identity for variable.",
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)
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name: str
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description: str = Field(default="", description="Description of the variable.")
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selector: Sequence[str] = Field(default_factory=list)
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class StringVariable(StringSegment, Variable):
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pass
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class FloatVariable(FloatSegment, Variable):
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pass
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class IntegerVariable(IntegerSegment, Variable):
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pass
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class ObjectVariable(ObjectSegment, Variable):
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pass
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class ArrayVariable(ArraySegment, Variable):
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pass
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class ArrayAnyVariable(ArrayAnySegment, ArrayVariable):
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pass
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class ArrayStringVariable(ArrayStringSegment, ArrayVariable):
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pass
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class ArrayNumberVariable(ArrayNumberSegment, ArrayVariable):
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pass
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class ArrayObjectVariable(ArrayObjectSegment, ArrayVariable):
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pass
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class SecretVariable(StringVariable):
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value_type: SegmentType = SegmentType.SECRET
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@property
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def log(self) -> str:
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return cast(str, encrypter.obfuscated_token(self.value))
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class NoneVariable(NoneSegment, Variable):
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value_type: SegmentType = SegmentType.NONE
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value: None = None
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class FileVariable(FileSegment, Variable):
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pass
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class ArrayFileVariable(ArrayFileSegment, ArrayVariable):
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pass
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# The `VariableUnion`` type is used to enable serialization and deserialization with Pydantic.
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# Use `Variable` for type hinting when serialization is not required.
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#
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# Note:
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# - All variants in `VariableUnion` must inherit from the `Variable` class.
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# - The union must include all non-abstract subclasses of `Segment`, except:
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VariableUnion: TypeAlias = Annotated[
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(
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Annotated[NoneVariable, Tag(SegmentType.NONE)]
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| Annotated[StringVariable, Tag(SegmentType.STRING)]
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| Annotated[FloatVariable, Tag(SegmentType.FLOAT)]
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| Annotated[IntegerVariable, Tag(SegmentType.INTEGER)]
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| Annotated[ObjectVariable, Tag(SegmentType.OBJECT)]
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| Annotated[FileVariable, Tag(SegmentType.FILE)]
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| Annotated[ArrayAnyVariable, Tag(SegmentType.ARRAY_ANY)]
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| Annotated[ArrayStringVariable, Tag(SegmentType.ARRAY_STRING)]
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| Annotated[ArrayNumberVariable, Tag(SegmentType.ARRAY_NUMBER)]
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| Annotated[ArrayObjectVariable, Tag(SegmentType.ARRAY_OBJECT)]
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| Annotated[ArrayFileVariable, Tag(SegmentType.ARRAY_FILE)]
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| Annotated[SecretVariable, Tag(SegmentType.SECRET)]
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),
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Discriminator(get_segment_discriminator),
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]
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