apischema

JSON (de)serialization, GraphQL and JSON schema generation using Python typing.

apischema makes your life easier when dealing with API data.

Documentation

https://wyfo.github.io/apischema/

Install

pip install apischema

It requires only Python 3.6+ (and dataclasses official backport for version 3.6 only)

PyPy3 is fully supported.

Why another library?

(If you wonder how this differs from the pydantic library, see the dedicated section of the documentation — there are many differences.)

This library fulfills the following goals:

• stay as close as possible to the standard library (dataclasses, typing, etc.) — as a consequence we do not need plugins for editors/linters/etc.;
• be adaptable, provide tools to support any types (ORM, etc.);
• avoid dynamic things like using raw strings for attributes name — play nicely with your IDE.

No known alternative achieves all of this, and apischema is also faster than all of them.

On top of that, because APIs are not only JSON, apischema is also a complete GraphQL library.

Example

from collections.abc import Collection
from dataclasses import dataclass, field
from uuid import UUID, uuid4

from graphql import print_schema
from pytest import raises

from apischema import ValidationError, deserialize, serialize
from apischema.graphql import graphql_schema
from apischema.json_schema import deserialization_schema


# Define a schema with standard dataclasses
@dataclass
class Resource:
    id: UUID
    name: str
    tags: set[str] = field(default_factory=set)


# Get some data
uuid = uuid4()
data = {"id": str(uuid), "name": "wyfo", "tags": ["some_tag"]}
# Deserialize data
resource = deserialize(Resource, data)
assert resource == Resource(uuid, "wyfo", {"some_tag"})
# Serialize objects
assert serialize(Resource, resource) == data
# Validate during deserialization
with raises(ValidationError) as err:  # pytest checks exception is raised
    deserialize(Resource, {"id": "42", "name": "wyfo"})
assert err.value.errors == [
    {"loc": ["id"], "msg": "badly formed hexadecimal UUID string"}
]
# Generate JSON Schema
assert deserialization_schema(Resource) == {
    "$schema": "http://json-schema.org/draft/2020-12/schema#",
    "type": "object",
    "properties": {
        "id": {"type": "string", "format": "uuid"},
        "name": {"type": "string"},
        "tags": {"type": "array", "items": {"type": "string"}, "uniqueItems": True},
    },
    "required": ["id", "name"],
    "additionalProperties": False,
}


# Define GraphQL operations
def resources(tags: Collection[str] | None = None) -> Collection[Resource] | None:
    ...


# Generate GraphQL schema
schema = graphql_schema(query=[resources], id_types={UUID})
schema_str = """\
type Query {
  resources(tags: [String!]): [Resource!]
}

type Resource {
  id: ID!
  name: String!
  tags: [String!]!
}
"""
assert print_schema(schema) == schema_str

apischema works out of the box with your data model.

Let's start the apischema tour.

Changelog

See releases