README.md

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 3.8+. PyPy3 is also 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.;
• avoid object-oriented limitations — do not require a base class — thus handle easily every type (Foo, list[Bar], NewType(Id, int), etc.) the same way.
• 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 (a lot) faster than all of them.

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

Actually, apischema is even adaptable enough to enable support of competitor libraries in a few dozens of line of code (pydantic support example using conversions feature)

Example

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

import pytest
from graphql import print_schema

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 pytest.raises(ValidationError) as err:  # pytest checks exception is raised
    deserialize(Resource, {"id": "42", "name": "wyfo"})
assert err.value.errors == [
    {"loc": ["id"], "err": "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,
            "default": [],
        },
    },
    "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.

This example and further ones are using pytest API because they are in fact run as tests in the library CI

Run the documentation examples

All documentation examples are written using the last Python minor version — currently 3.10 — in order to provide up-to-date documentation. Because Python 3.10 specificities (like PEP 585) are used, this version is "mandatory" to execute the examples as-is.

In addition to pytest, some examples use third-party libraries like SQLAlchemy or attrs. All of this dependencies can be downloaded using the examples extra with

pip install apischema[examples]

Once dependencies are installed, you can simply copy-paste examples and execute them, using the proper Python version.