9797fb35d9
* 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default handling * Update `StringTypeParser` to refine `format` handling and enrich `json_schema_extra` * Remove outdated `oneOf` examples from docs, expand test cases and provide refined examples with discriminator support
50 lines
1.4 KiB
ReStructuredText
50 lines
1.4 KiB
ReStructuredText
Using Jambo
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===================
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Jambo is designed to be easy to use, it doesn't require any complex setup or configuration.
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Below a example of how to use Jambo to convert a JSON Schema into a Pydantic model.
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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": "Person",
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"type": "object",
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"properties": {
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"name": {"type": "string"},
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"age": {"type": "integer"},
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},
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"required": ["name"],
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}
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Person = SchemaConverter.build(schema)
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obj = Person(name="Alice", age=30)
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print(obj)
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# Output: Person(name='Alice', age=30)
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The :py:meth:`SchemaConverter.build <jambo.SchemaConverter.build>` static method takes a JSON Schema dictionary and returns a Pydantic model class. You can then instantiate this class with the required fields, and it will automatically validate the data according to the schema.
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If passed a description inside the schema it will also add it to the Pydantic model using the `description` field. This is useful for AI Frameworks as: LangChain, CrewAI and others, as they use this description for passing context to LLMs.
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For more complex schemas and types see our documentation on
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.. toctree::
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:maxdepth: 2
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:caption: Contents:
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usage.string
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usage.numeric
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usage.bool
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usage.array
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usage.object
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usage.reference
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usage.allof
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usage.anyof
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usage.oneof
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usage.enum
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usage.const |