JSONClasses is a data flow pipeline and data graph framework written in Python. It supports data sanitization, data validation, data transformation, data presentation, data serialization, data graphing and data querying. It eliminates the redundant coding of the process by an intuitive and innovative declarative manner.

How JSONClasses Works?

JSONClasses uses several Python features like type hinting and dataclasses. With the great metaprogramming functionalities that Python offers, we can easily extend it into a great DSL for declaring data structures, transforming rules and validation rules.

Why Not Create Another DSL?

GraphQL's Schema Definition Language cannot work well with programming languages' syntax checking and type completion. To support more and more functions, a DSL would become more and more like a programming language.

This is similar React.js, Jetpack Compose and SwiftUI. The structures of the declaration is embedded in code, not in a special text file.

Why Python Is Chosen?

Python is the programming language which is nearest to AI areas. The era we are living is an era and a generation empowered by AI. AI algorithms empower products with unimaginable stunning features. A great product should adapt to some level of AI to continue providing great functions for it's targeting audience.

Business Logic Examples

Example 1: Dating App Users

Let's say, you are building the base user functionality for a cross-platform dating app.

The product requirements are:

  1. Unique phone number is required
  2. Password should be secure, encrypted, hidden from response
  3. Gender cannot be changed after set
  4. This product is adult only
  5. User intro should be brief

Let's transform the requirements into code.

from jsonclasses import jsonclass, types

class User:
  phone_no: str = types.str.unique.index.match(local_phone_no_regex).required #1
  email: str = types.str.match(email_regex)
  password: str = types.str.writeonly.length(8, 16).match(secure_password_regex).transform(salt).required #2
  nickname: str = types.str.required
  gender: str = types.str.writeonce.oneof(['male', 'female']) #3
  age: int = types.int.min(18).max(100) #4
  intro: str = types.str.truncate(500) #5

Look how brief it is to describe our business requirements. JSON Classes has official support for some databases to store data permanently. If you are building a RESTful API, you can integrate JSON Classes with flask or any other web frameworks.

from flask import Blueprint, request, jsonify
from models.article import Article

bp = Blueprint('articles', __name__, url_prefix='/articles')

async def articles(request: Request):
  return jsonify(await User.find())

async def user(request, id):
  return jsonify(await User.id(id))

async def create_user(request):
  return jsonify(User(**request.json).save())

async def update_user(request, id):
  return jsonify((await User.id(id)).set(**request.json).save())

async def delete_user(request, id):
  return jsonify((await User.id(id)).delete())


Read the Documentation

Database & Web Framework Integrations

Supported Python Versions

jsonclasses supports Python >= 3.9.0.


GitHub - fillmula/jsonclasses: The Modern Declarative Data Flow Framework for the AI Empowered Generation.
The Modern Declarative Data Flow Framework for the AI Empowered Generation. - GitHub - fillmula/jsonclasses: The Modern Declarative Data Flow Framework for the AI Empowered Generation.