# JSON Schema to Pydantic
A Python library for automatically generating Pydantic v2 models from JSON Schema definitions.
![PyPI - Version](https://img.shields.io/pypi/v/json-schema-to-pydantic)
[![codecov](https://codecov.io/github/richard-gyiko/json-schema-to-pydantic/graph/badge.svg?token=YA2Y769H1K)](https://codecov.io/github/richard-gyiko/json-schema-to-pydantic)
## Features
- Converts JSON Schema to Pydantic v2 models
- Supports complex schema features including:
- References ($ref) with circular reference detection
- Combiners (allOf, anyOf, oneOf) with proper type discrimination
- Type constraints and validations
- Array and object validations
- Format validations (email, uri, uuid, date-time)
- Full type hinting support
- Clean, simple API
## Installation
```bash
pip install json-schema-to-pydantic
```
## Development Setup
1. Clone the repository
2. Install development dependencies:
```bash
# Using uv (recommended)
uv pip install -e ".[dev]"
# Or using pip
pip install -e ".[dev]"
```
3. Run tests:
```bash
pytest
```
## Quick Start
```python
from json_schema_to_pydantic import create_model
# Define your JSON Schema
schema = {
"title": "User",
"type": "object",
"properties": {
"name": {"type": "string"},
"email": {"type": "string", "format": "email"},
"age": {"type": "integer", "minimum": 0}
},
"required": ["name", "email"]
}
# Generate your Pydantic model
UserModel = create_model(schema)
# Use the model
user = UserModel(
name="John Doe",
email="john@example.com",
age=30
)
```
## Advanced Usage
For more complex scenarios, you can use the `PydanticModelBuilder` directly:
```python
from json_schema_to_pydantic import PydanticModelBuilder
builder = PydanticModelBuilder()
model = builder.create_pydantic_model(schema, root_schema)
```
## Error Handling
The library provides specific exceptions for different error cases:
```python
from json_schema_to_pydantic import (
SchemaError, # Base class for all schema errors
TypeError, # Invalid or unsupported type
CombinerError, # Error in schema combiners
ReferenceError, # Error in schema references
)
try:
model = create_model(schema)
except TypeError as e:
print(f"Invalid type in schema: {e}")
except ReferenceError as e:
print(f"Invalid reference: {e}")
```
## Documentation
See [docs/features.md](docs/features.md) for detailed documentation of supported JSON Schema features.
## Contributing
1. Fork the repository
2. Create a new branch for your feature
3. Make your changes
4. Run tests and ensure they pass
5. Submit a pull request
## License
This project is licensed under the terms of the license included in the repository.
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"description": "# JSON Schema to Pydantic\n\nA Python library for automatically generating Pydantic v2 models from JSON Schema definitions.\n\n![PyPI - Version](https://img.shields.io/pypi/v/json-schema-to-pydantic)\n[![codecov](https://codecov.io/github/richard-gyiko/json-schema-to-pydantic/graph/badge.svg?token=YA2Y769H1K)](https://codecov.io/github/richard-gyiko/json-schema-to-pydantic)\n\n## Features\n\n- Converts JSON Schema to Pydantic v2 models\n- Supports complex schema features including:\n - References ($ref) with circular reference detection\n - Combiners (allOf, anyOf, oneOf) with proper type discrimination\n - Type constraints and validations\n - Array and object validations\n - Format validations (email, uri, uuid, date-time)\n- Full type hinting support\n- Clean, simple API\n\n## Installation\n\n```bash\npip install json-schema-to-pydantic\n```\n\n## Development Setup\n\n1. Clone the repository\n2. Install development dependencies:\n```bash\n# Using uv (recommended)\nuv pip install -e \".[dev]\"\n\n# Or using pip\npip install -e \".[dev]\"\n```\n\n3. Run tests:\n```bash\npytest\n```\n\n## Quick Start\n\n```python\nfrom json_schema_to_pydantic import create_model\n\n# Define your JSON Schema\nschema = {\n \"title\": \"User\",\n \"type\": \"object\",\n \"properties\": {\n \"name\": {\"type\": \"string\"},\n \"email\": {\"type\": \"string\", \"format\": \"email\"},\n \"age\": {\"type\": \"integer\", \"minimum\": 0}\n },\n \"required\": [\"name\", \"email\"]\n}\n\n# Generate your Pydantic model\nUserModel = create_model(schema)\n\n# Use the model\nuser = UserModel(\n name=\"John Doe\",\n email=\"john@example.com\",\n age=30\n)\n```\n\n## Advanced Usage\n\nFor more complex scenarios, you can use the `PydanticModelBuilder` directly:\n\n```python\nfrom json_schema_to_pydantic import PydanticModelBuilder\n\nbuilder = PydanticModelBuilder()\nmodel = builder.create_pydantic_model(schema, root_schema)\n```\n\n## Error Handling\n\nThe library provides specific exceptions for different error cases:\n\n```python\nfrom json_schema_to_pydantic import (\n SchemaError, # Base class for all schema errors\n TypeError, # Invalid or unsupported type\n CombinerError, # Error in schema combiners\n ReferenceError, # Error in schema references\n)\n\ntry:\n model = create_model(schema)\nexcept TypeError as e:\n print(f\"Invalid type in schema: {e}\")\nexcept ReferenceError as e:\n print(f\"Invalid reference: {e}\")\n```\n\n## Documentation\n\nSee [docs/features.md](docs/features.md) for detailed documentation of supported JSON Schema features.\n\n## Contributing\n\n1. Fork the repository\n2. Create a new branch for your feature\n3. Make your changes\n4. Run tests and ensure they pass\n5. Submit a pull request\n\n## License\n\nThis project is licensed under the terms of the license included in the repository.\n",
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