Metadata-Version: 2.1
Name: apischema
Version: 0.19.0
Summary: JSON (de)serialization, GraphQL and JSON schema generation using Python typing.
Author-email: Joseph Perez <joperez@hotmail.fr>
License: MIT
Project-URL: Repository, https://github.com/wyfo/apischema
Project-URL: Documentation, https://wyfo.github.io/apischema
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Provides-Extra: examples
Requires-Dist: graphql-core>=3.0.0; extra == "examples"
Requires-Dist: attrs; extra == "examples"
Requires-Dist: docstring-parser; extra == "examples"
Requires-Dist: bson; extra == "examples"
Requires-Dist: orjson; extra == "examples"
Requires-Dist: pydantic; extra == "examples"
Requires-Dist: pytest; extra == "examples"
Requires-Dist: sqlalchemy; extra == "examples"
Provides-Extra: graphql
Requires-Dist: graphql-core>=3.0.0; extra == "graphql"

# 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/](https://wyfo.github.io/apischema/)

## Install
```shell
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](https://wyfo.github.io/apischema/dev/difference_with_pydantic) — 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](https://wyfo.github.io/apischema/dev/optimizations_and_benchmark#benchmark) 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](https://wyfo.github.io/apischema/dev/examples/pydantic_support) using [conversions feature](https://wyfo.github.io/apischema/dev/conversions))

## Example

```python
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](https://www.python.org/dev/peps/pep-0604/)) 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
```shell
pip install apischema[examples]
```

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