JSON to Pydantic with Validators Generator

Generate Pydantic v2 BaseModel classes with Field constraints and @field_validator decorators from JSON data.

What is JSON to Pydantic with Validators Generator?

This tool converts JSON data into Pydantic v2 BaseModel classes with automatic type inference, Field constraints, and @field_validator decorators. It generates production-ready validation code for emails, URLs, phone numbers, string lengths, number ranges, and list sizes.

How to Use

  1. Paste your JSON object or array into the input area
  2. Configure basic options: root class name, snake_case conversion, null handling, date detection
  3. Click "Validators ▼" to expand validator options
  4. Enable auto-detection for emails, URLs, or phone numbers
  5. Set constraints for strings (min/max length), numbers (min/max, positive only), and lists (min/max items)
  6. Click "Generate" to create the Pydantic code with validators
  7. Copy the output and use it in your Python project

Why Use This Tool?

Automatic type inference from JSON values
Field constraints using Field() with min_length, max_length, gt, ge, lt, le
Auto-generated @field_validator decorators for emails, URLs, and phone numbers
Modern Python 3.10+ syntax with built-in generics
Nested model generation with correct class ordering
Field alias support for camelCase to snake_case conversion
Pydantic v2 compatible with ConfigDict and field_validator

Tips & Best Practices

  • Use realistic sample data for accurate type inference and validator detection
  • Enable snake_case for idiomatic Python field names
  • Email validator uses regex: ^[\w\.-]+@[\w\.-]+\.\w+$
  • URL validator checks for http:// or https:// prefix
  • Phone validator matches international format: +?[\d\s\-()]{10,}
  • Set number constraints to enforce business rules (e.g., age > 0, score <= 100)
  • List constraints help validate array sizes (e.g., tags must have 1-10 items)

Frequently Asked Questions

What is Pydantic?

Pydantic is a Python library for data validation using Python type annotations. It provides automatic data parsing, validation, serialization, and generates JSON schemas. It is widely used with FastAPI, Django, and other frameworks.

How are JSON types mapped to Python?

JSON strings become str, integers become int, floats become float, booleans become bool, null becomes T | None, arrays become list[T], and nested objects become separate BaseModel classes.

What validators are generated?

Email fields get @field_validator with regex validation. URL fields get a validator checking http:// or https:// prefix. Phone fields get a validator matching international phone format. All validators raise ValueError with descriptive messages.

What are Field constraints?

Field constraints are validation rules applied directly in the Field() declaration. Strings can have min_length and max_length. Numbers can have gt (greater than), ge (greater or equal), lt (less than), le (less or equal). Lists can have min_length and max_length for item count.

Why use snake_case conversion?

Python convention uses snake_case for variable and attribute names, while JSON often uses camelCase. This option converts field names and adds Field(alias="...") so the original JSON keys still work during deserialization.

Is my data sent to a server?

No, all processing happens entirely in your browser. Your JSON data never leaves your device.

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