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JSON to Python Pydantic v2 & Dataclass Code Generator

Transform raw JSON payloads into production-ready Python Pydantic v2 BaseModel classes, TypedDict, or standard @dataclass schemas with type hints and alias support.

Schema Standard:
Root Class Name:
Type Union Syntax:
Field Case Style:
Samples:
Source JSON Payload33 lines
Generated Python Models (Python 3.10+)
Generated Classes4
Total Fields20
Code Lines36
FastAPI ReadyValid

100% Client-Side Privacy

Your API payloads, user records, and production schemas are transpiled directly in your browser without external servers.

Pydantic v2 Architecture

Native support for BaseModel, Field(alias=...), nested dependency ordering, and modern Python 3.10+ union pipe syntax.

Multi-Paradigm Export

Easily toggle between Pydantic v2 schemas, lightweight TypedDict definitions, or standard Python @dataclass models.

Technical Reference & Architecture Guide

JSON to Python Pydantic v2 & Dataclass Developer Guide — Reference Guide

Complete architecture guide for generating modern Python 3.10+ Pydantic v2 BaseModel schemas, TypedDict, and dataclasses from arbitrary JSON payloads with type inference and field aliases.

Key Parameters & Controls

rootModelNamestring

Name assigned to the primary top-level Pydantic class generated from the JSON payload.

targetFrameworkstring ('pydantic_v2' | 'typeddict' | 'dataclass')

Target Python typing paradigm and validation framework to generate.

unionSyntaxstring ('pipe' | 'optional')

Use modern Python 3.10+ pipe syntax (int | None) versus legacy typing.Optional[int].

fieldNamingstring ('snake_case' | 'original')

Automatically convert camelCase JSON keys into idiomatic Python snake_case with Field(alias=...).

Pro Tips & Best Practices

In Pydantic v2, prefer model_validate(data) and model_dump() over obsolete v1 parse_obj() and dict() methods.

When dealing with camelCase APIs, keep snake_case field names and leverage Field(validation_alias='camelCase') for clean Python code.

Detect ISO 8601 strings and map them to datetime.datetime for native timezone-aware validation in FastAPI applications.

Common Gotchas & Troubleshooting

Empty arrays default to list[Any]. Provide a sample with at least one representative item to infer specific types.

The generator automatically suffixes colliding keywords (e.g., from_ or type_) and attaches the original key as an alias.

Frequently Asked Questions

Does the generated code target Pydantic v2 or v1?▼

By default, the code is strictly optimized for modern Pydantic v2 syntax, avoiding legacy v1 deprecations.

How are nullable and missing fields handled?▼

Null values are inferred as Optional types with default=None (e.g., str | None = None).

Can I use the generated schemas directly inside FastAPI routes?▼

Yes. Pydantic v2 BaseModel classes integrate directly into FastAPI request bodies and response models.

Is my JSON payload uploaded to any cloud backend?▼

No. The AST inference engine runs completely in client-side TypeScript inside your browser.

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