Skip to main content

Documentation Index

Fetch the complete documentation index at: https://askmarvin.ai/llms.txt

Use this file to discover all available pages before exploring further.

​
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: 
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

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

​
Classes

​
JSONSchema

class JSONSchema()

​
Functions

​
create_array_type

def create_array_type(schema: Mapping[str, Any], schemas: Mapping[str, Any]) -> type | Annotated[Any, ...]
Create list/set type with optional constraints.

​
create_dataclass

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

def create_enum(name: str, values: list[Any]) -> type | Enum
Create enum type from list of values.

​
create_field_with_default

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

def create_numeric_type(base: Type[int | float], schema: Mapping[str, Any]) -> type | Annotated[Any, ...]
Create numeric type with optional constraints.

​
create_string_type

def create_string_type(schema: Mapping[str, Any]) -> type | Annotated[Any, ...]
Create string type with optional constraints.

​
get_default_value

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

def hash_schema(schema: Mapping[str, Any]) -> str
Generate a deterministic hash for schema caching.

​
jsonschema_to_type

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: 
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: 
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

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

def resolve_ref(ref: str, schemas: Mapping[str, Any]) -> Mapping[str, Any]
Resolve JSON Schema reference to target schema.

​
sanitize_name

def sanitize_name(name: str) -> str
Convert string to valid Python identifier.

​
schema_to_type

def schema_to_type(schema: Mapping[str, Any], schemas: Mapping[str, Any]) -> type
Convert schema to appropriate Python type.
Parent Module: utilities