9aec7c3e3b
* Add support for `oneOf` type parsing with validation and example cases * Improve `oneOf` type parsing: refine validators, add discriminator support, and expand test coverage * Add hashable and non-hashable value support to `ConstTypeParser` with expanded test cases * Refine `field_props` check in `_type_parser` for cleaner default handling * Update `StringTypeParser` to refine `format` handling and enrich `json_schema_extra` * Remove outdated `oneOf` examples from docs, expand test cases and provide refined examples with discriminator support
109 lines
3.3 KiB
ReStructuredText
109 lines
3.3 KiB
ReStructuredText
OneOf Type
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=================
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The OneOf type is used to specify that an object must conform to exactly one of the specified schemas. Unlike AnyOf which allows matching multiple schemas, OneOf enforces that the data matches one and only one of the provided schemas.
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Examples
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-----------------
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1. **Overlapping String Example** - A field that accepts strings with overlapping constraints:
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.. code-block:: python
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from jambo import SchemaConverter
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schema = {
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"title": "SimpleExample",
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"type": "object",
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"properties": {
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"value": {
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"oneOf": [
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{"type": "string", "maxLength": 6},
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{"type": "string", "minLength": 4}
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]
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}
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},
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"required": ["value"]
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}
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Model = SchemaConverter.build(schema)
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# Valid: Short string (matches first schema only)
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obj1 = Model(value="hi")
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print(obj1.value) # Output: hi
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# Valid: Long string (matches second schema only)
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obj2 = Model(value="very long string")
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print(obj2.value) # Output: very long string
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# Invalid: Medium string (matches BOTH schemas - violates oneOf)
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try:
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obj3 = Model(value="hello") # 5 chars: matches maxLength=6 AND minLength=4
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except ValueError as e:
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print("Validation fails as expected:", e)
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2. **Discriminator Example** - Different shapes with a type field:
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.. code-block:: python
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from jambo import SchemaConverter
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schema = {
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"title": "Shape",
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"type": "object",
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"properties": {
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"shape": {
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"oneOf": [
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{
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"type": "object",
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"properties": {
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"type": {"const": "circle"},
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"radius": {"type": "number", "minimum": 0}
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},
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"required": ["type", "radius"]
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},
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{
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"type": "object",
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"properties": {
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"type": {"const": "rectangle"},
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"width": {"type": "number", "minimum": 0},
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"height": {"type": "number", "minimum": 0}
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},
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"required": ["type", "width", "height"]
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}
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],
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"discriminator": {
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"propertyName": "type"
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}
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}
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},
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"required": ["shape"]
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}
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Model = SchemaConverter.build(schema)
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# Valid: Circle
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circle = Model(shape={"type": "circle", "radius": 5.0})
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print(circle.shape.type) # Output: circle
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# Valid: Rectangle
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rectangle = Model(shape={"type": "rectangle", "width": 10, "height": 20})
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print(rectangle.shape.type) # Output: rectangle
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# Invalid: Wrong properties for the type
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try:
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invalid = Model(shape={"type": "circle", "width": 10})
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except ValueError as e:
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print("Validation fails as expected:", e)
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.. note::
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OneOf ensures exactly one schema matches. The discriminator helps Pydantic efficiently determine which schema to use based on a specific property value.
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.. warning::
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If your data could match multiple schemas in a oneOf, validation will fail. Ensure schemas are mutually exclusive.
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