> ## Documentation Index
> Fetch the complete documentation index at: https://askmarvin.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# jsonschema

> Convert JSON Schema to Python types with validation.

# `marvin.utilities.jsonschema`

Convert JSON Schema to Python types with validation.

The jsonschema\_to\_type function converts a JSON Schema into a Python type that can be used
for validation with Pydantic. It supports:

* Basic types (string, number, integer, boolean, null)
* Complex types (arrays, objects)
* Format constraints (date-time, email, uri)
* Numeric constraints (minimum, maximum, multipleOf)
* String constraints (minLength, maxLength, pattern)
* Array constraints (minItems, maxItems, uniqueItems)
* Object properties with defaults
* References and recursive schemas
* Enums and constants
* Union types

Example:

```python theme={null}
schema = {
    "type": "object",
    "properties": {
        "name": {"type": "string", "minLength": 1},
        "age": {"type": "integer", "minimum": 0},
        "email": {"type": "string", "format": "email"}
    },
    "required": ["name", "age"]
}

# Name is optional and will be inferred from schema's "title" property if not provided
Person = jsonschema_to_type(schema)
# Creates a validated dataclass with name, age, and optional email fields
```

## Constants

### `FORMAT_TYPES`

```python theme={null}
FORMAT_TYPES = {'date-time': datetime, 'email': EmailStr, 'uri': AnyUrl, 'json': Json}
```

## Classes

### `JSONSchema`

```python theme={null}
class JSONSchema()
```

## Functions

### `create_array_type`

```python theme={null}
def create_array_type(schema: Mapping[str, Any], schemas: Mapping[str, Any]) -> type | Annotated[Any, ...]
```

Create list/set type with optional constraints.

### `create_dataclass`

```python theme={null}
def create_dataclass(schema: Mapping[str, Any], name: str | None = None, schemas: Mapping[str, Any] | None = None) -> type
```

Create dataclass from object schema.

### `create_enum`

```python theme={null}
def create_enum(name: str, values: list[Any]) -> type | Enum
```

Create enum type from list of values.

### `create_field_with_default`

```python theme={null}
def create_field_with_default(field_type: type, default_value: Any, schema: dict[str, Any]) -> Any
```

Create a field with simplified default handling.

### `create_numeric_type`

```python theme={null}
def create_numeric_type(base: Type[int | float], schema: Mapping[str, Any]) -> type | Annotated[Any, ...]
```

Create numeric type with optional constraints.

### `create_string_type`

```python theme={null}
def create_string_type(schema: Mapping[str, Any]) -> type | Annotated[Any, ...]
```

Create string type with optional constraints.

### `get_default_value`

```python theme={null}
def get_default_value(schema: dict[str, Any], prop_name: str, parent_default: dict[str, Any] | None = None) -> Any
```

Get default value with proper priority ordering.

1. Value from parent's default if it exists
2. Property's own default if it exists
3. None

### `hash_schema`

```python theme={null}
def hash_schema(schema: Mapping[str, Any]) -> str
```

Generate a deterministic hash for schema caching.

### `jsonschema_to_type`

```python theme={null}
def jsonschema_to_type(schema: Mapping[str, Any], name: str | None = None) -> type
```

Convert JSON schema to appropriate Python type with validation.

Args:
schema: A JSON Schema dictionary defining the type structure and validation rules
name: Optional name for object schemas. Only allowed when schema type is "object".
If not provided for objects, name will be inferred from schema's "title"
property or default to "Root".

Returns:
A Python type (typically a dataclass for objects) with Pydantic validation

Raises:
ValueError: If a name is provided for a non-object schema

Examples:
Create a dataclass from an object schema:

```python theme={null}
schema = {
    "type": "object",
    "title": "Person",
    "properties": {
        "name": {"type": "string", "minLength": 1},
        "age": {"type": "integer", "minimum": 0},
        "email": {"type": "string", "format": "email"}
    },
    "required": ["name", "age"]
}

Person = jsonschema_to_type(schema)
# Creates a dataclass with name, age, and optional email fields:
# @dataclass
# class Person:
#     name: str
#     age: int
#     email: str | None = None
```

Person(name="John", age=30)

Create a scalar type with constraints:

```python theme={null}
schema = {
    "type": "string",
    "minLength": 3,
    "pattern": "^[A-Z][a-z]+$"
}

NameType = jsonschema_to_type(schema)
# Creates Annotated[str, StringConstraints(min_length=3, pattern="^[A-Z][a-z]+$")]

@dataclass
class Name:
    name: NameType
```

### `merge_defaults`

```python theme={null}
def merge_defaults(data: Mapping[str, Any], schema: Mapping[str, Any], parent_default: Mapping[str, Any] | None = None) -> dict[str, Any]
```

Merge defaults with provided data at all levels.

### `resolve_ref`

```python theme={null}
def resolve_ref(ref: str, schemas: Mapping[str, Any]) -> Mapping[str, Any]
```

Resolve JSON Schema reference to target schema.

### `sanitize_name`

```python theme={null}
def sanitize_name(name: str) -> str
```

Convert string to valid Python identifier.

### `schema_to_type`

```python theme={null}
def schema_to_type(schema: Mapping[str, Any], schemas: Mapping[str, Any]) -> type
```

Convert schema to appropriate Python type.

***

**Parent Module:** [`utilities`](marvin-utilities)