japyd 1.1.4

Automate JSON:API management in Flask. No manual mapping, just clean code.

japyd

"JSON:API, Pydantically simple."

Automate JSON:API relationships with Pydantic. No manual mapping, just clean code.

Japyd: Core Value Propositions

Japyd is a Python toolkit designed to simplify the interaction with JSON:API, a specification for building APIs in JSON. It provides a set of utilities to facilitate the creation, manipulation, and validation of JSON:API resources and requests.

Build JSON:API Relational Structures from Pydantic Models

Native Pydantic Integration: Automatically generates JSON:API-compliant resources and relationships from Pydantic models, eliminating boilerplate.
Relationship Management: Supports to-one, to-many, and included resources, fully compliant with the JSON:API specification.
Smart Serialization: Converts Pydantic objects into valid JSON:API documents, handling attributes, relationships, and metadata seamlessly.

Parse JSON:API Query Parameters (Dotnet-style Syntax)

Advanced Query Parsing: Supports JSON:API.NET-style query parameters, including filtering, sorting, pagination, sparse fieldsets, and inclusion.
Built-in Validation: Ensures query parameters are syntactically correct and consistent.

• Filtering: filter[name]=Guillaume
• Sorting: sort=-created,title
• Pagination: page[number]=1&page[size]=10
• Sparse Fieldsets: fields[articles]=title,author
• Inclusion: include=author,comments

Flask Extension for JSON:API Response Formatting

Ready-to-use Flask Integration: Provides a Flask extension to automatically encapsulate responses in JSON:API format, including:

• Resources (data, relationships, attributes)
• Pagination (links, page metadata)
• Errors (standardized JSON:API error objects)

Simplified Endpoints: Reduces manual response formatting, ensuring compliance with JSON:API standards.

Relationship decomposition

To automate and standardize the definition of relationships in our JSON:API implementation, we leveraged Pydantic’s data model.
This approach allowed us to dynamically infer relationships between resources without manually declaring them for each object type.

The principle is as follows:

• A Pydantic model represents a resource (e.g., JSON:API Resource Object).
• If an attribute of this model is itself a Pydantic object (or a list of Pydantic objects), it is automatically interpreted as a relationship in the JSON:API response.
• If the attribute is a primitive type (string, integer, boolean, or event dict etc.), it is treated as a standard attribute.

Usage

Serialization

Define your data models in Pydantic, let japyd automatically handle serialization—including relationships and included resources—and expose a standard-compliant JSON:API with Flask in just a few lines of code.

import typing as t

import pytest
from flask import Flask
from flask_pydantic import validate

from japyd import JsonApiBaseModel
from japyd import JsonApiQueryModel
from japyd import TopLevel


class Product(JsonApiBaseModel):
    jsonapi_type: t.ClassVar[str] = "product"

    id: str
    price: float


class Order(JsonApiBaseModel):
    jsonapi_type: t.ClassVar[str] = "order"

    id: str
    customer_id: str
    items: list[Product]  # This field will be 'relationship' in JSON:API
    status: str  # This field will be classical 'attribute'


app = Flask(__name__)


@app.route("/orders/<order_id>")
@validate(exclude_none=True)
def get_order(order_id, query: JsonApiQueryModel):
    order = Order(id=order_id, customer_id="123", items=[Product(id="1", price=100.0)], status="open")
    return query.one_or_none(order)


@pytest.fixture()
def client():
    return app.test_client()


def test_request(client):
    response = client.get("/orders/3?include=items")
    top = TopLevel.model_validate(response.json)
    assert top.data.id == "3"
    assert top.data.attributes['status'] == 'open'
    assert len(top.data.relationships['items'].data) == 1
    assert top.included[0].type == "product"

You can bypass this behavior by annotationg the field as follow:

    items: Annotated[list[Product], 'as_attribute']  # This field will be now an 'attribute' in JSON:API

Deserialization

Deserialization of JSON:API resource objects into flat dictionaries is handled by the flatten_resource function. This function extracts resource attributes along with the id and type fields, and can optionally flatten nested relationships using a pattern parameter.

from japyd import TopLevel, flatten_resource, extract_relationship

# Example: flatten a resource with nested relationships
response = client.get("/orders/3?include=items")
top = TopLevel.model_validate(response.json)

# Flatten the resource to a dictionary
flattened = flatten_resource(top.data)
# Result: {"type": "order", "id": "3", "customer_id": "123", "status": "open"}

# Flatten with nested relationships using pattern
flattened_with_items = flatten_resource(
    top.data, 
    toplevel=top, 
    pattern="items"
)
# Result: {"type": "order", "id": "3", "customer_id": "123", "status": "open", 
#          "items": [{"type": "product", "id": "1", "price": 100.0}]}

Filtering

The complete filtering syntax of JsonApiDotNetCore is supported

References

japyd (JsonApi PYDantic) is a coherent and powerful composition of :

1. Pydantic and its Flask extension Flask-Pydantic
2. Filtering syntax defined in the dotnet implementation JsonApiDotNetCore.
3. Simple relationship extraction and other structure manipulations.

🚀 Looking for Contributors

We’re actively seeking developers, testers, and open-source enthusiasts to help us build and improve japyd. Whether you’re passionate about data validation, API design, or just want to contribute to an innovative open-source project, your help is welcome! Check out our contribution guidelines and open issues to get started. Let’s shape the future of Python APIs together! 💻✨