Features

🐍 Python Type Hints

Automatic type inference
Optional field support
Nested model generation
List/Dict type annotations

✅ Data Validation

Runtime type checking
Custom validators
Field constraints
JSON Schema generation

🚀 Framework Ready

FastAPI integration
Django REST compatible
SQLAlchemy models
OpenAPI documentation

JSON to Pydantic Guide

Pydantic is the data validation library behind FastAPI, and its models double as both type-checked Python classes and self-documenting schemas. Hand-writing a Pydantic model for every JSON shape your API sends or receives gets tedious fast — this tool infers field types directly from a sample payload (str, int, float, bool, nested models for objects, List[...] for arrays) and outputs a ready-to-paste BaseModel class.

By default it generates Pydantic v2 syntax (the current stable release, with model_config and updated validator decorators); uncheck "V2 Syntax" for v1-compatible output if you're maintaining an older codebase. Enabling "Make all fields Optional" wraps every field in Optional[...] = None — useful for PATCH request bodies where any subset of fields may be present.

Frequently Asked Questions

How are nested objects and arrays handled?

Nested JSON objects become their own BaseModel subclasses referenced by name, and arrays of objects become List[YourModel] — so a deeply nested API response produces a full tree of properly typed, reusable model classes, not one flat class with dict fields.

What does "Add example validators" do?

It inserts commented-out @field_validator examples on string and numeric fields, showing the syntax for common checks (non-empty strings, positive numbers) so you can adapt them to your actual constraints instead of starting from a blank Pydantic docs page.

Can I use the generated models directly with FastAPI?

Yes — a generated BaseModel can be used as-is for request/response typing in FastAPI route handlers, and FastAPI will automatically derive OpenAPI/Swagger documentation and validation from it.

Working with generated Pydantic models

Pydantic validates data as it builds your objects, so a model generated from sample JSON doubles as runtime documentation of what your service accepts. Review it once and it will catch bad input for you from then on.

Tips and common pitfalls

Use Optional[str] = None (or str | None = None) for fields that are not always present.
Replace plain str with stricter types where they fit: EmailStr, HttpUrl, datetime or a Literal of allowed values.
Add model_config = ConfigDict(extra="forbid") to reject unexpected keys in request bodies.
Use Field(alias="userName") when the JSON uses camelCase but your Python code uses snake_case.

JSON to Pydantic Model