Metadata-Version: 2.4
Name: pydantic-jsonschema
Version: 0.0.8
Summary: JSON Schema to Pydantic model conversion library
Project-URL: Homepage, https://github.com/Danipulok/pydantic-jsonschema
Project-URL: Documentation, https://danipulok.github.io/pydantic-jsonschema/
Project-URL: Repository, https://github.com/Danipulok/pydantic-jsonschema
Project-URL: Issues, https://github.com/Danipulok/pydantic-jsonschema/issues
Project-URL: Changelog, https://github.com/Danipulok/pydantic-jsonschema/blob/main/docs/changelog.md
Author-email: Danipulok <danipulok@gmail.com>
License: MIT
License-File: LICENSE
Keywords: ai,json-schema,llm,pydantic,schema,type-safety,validation
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Typing :: Typed
Requires-Python: >=3.12
Requires-Dist: pydantic>=2.13.0
Description-Content-Type: text/markdown

# Pydantic JSON Schema

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Convert JSON Schema definitions into Pydantic models with runtime validation.

## Installation

```bash
uv add pydantic-jsonschema
```

Requires Python 3.12+.
See the [installation guide](https://danipulok.github.io/pydantic-jsonschema/latest/install/) for third-party validator libraries.

## Quick Start

```python
from pydantic_jsonschema import Schema, to_model

schema = Schema.model_validate({
    "type": "object",
    "properties": {
        "name": {"type": "string"},
        "age": {"type": "integer", "minimum": 0},
    },
    "required": ["name"],
})

User = to_model(schema, model_name="User")

user = User(name="Alice", age=30)
print(user.model_dump())
#> {'name': 'Alice', 'age': 30}
```

## Schema Model

The `Schema` class is a Pydantic model representing a JSON Schema object.
Use it to parse, inspect, and serialize schemas with full type safety:

```python
from pydantic_jsonschema import DataType, Schema

schema = Schema(
    type=DataType.OBJECT,
    properties={
        "name": Schema(type=DataType.STRING),
    },
    required=["name"],
)

print(schema.model_dump_json(indent=4))
"""
{
    "type": "object",
    "properties": {
        "name": {
            "type": "string"
        }
    },
    "required": [
        "name"
    ]
}
"""
```

`Schema` supports `$ref` and `$defs` for reusable definitions — properties referencing a `$ref` are parsed as `Reference` objects and resolved automatically during conversion.

See the [Schema documentation](https://danipulok.github.io/pydantic-jsonschema/latest/schema/)
for field reference and usage examples.

## Documentation

[https://danipulok.github.io/pydantic-jsonschema/](https://danipulok.github.io/pydantic-jsonschema/)

## Roadmap

- [x] Custom JSON Schema implementation (remove `openapi-pydantic` dependency)
- [x] Remove `fqdn` and `rfc3986` dependencies
- [x] Remove `email-validator` dependency
- [x] Logic fixes in schema conversion
- [x] Add `inline_snapshot` for tests
- [ ] Configurable loading by type (inspired by [adaptix](https://github.com/reagento/adaptix))

## Acknowledgments

- [vgavro](https://github.com/vgavro) — initial library idea, guidance, and ongoing support
- [bodlan](https://github.com/bodlan) — contributions to documentation and conversion logic

## License

MIT License - see [LICENSE](LICENSE) for details.
