JSON to Pydantic Model

Frequently Asked Questions

What is Pydantic?

Pydantic is the most popular data validation library for Python. It validates data at runtime using Python type annotations and is the foundation of FastAPI, LangChain, and much of the modern Python ML/API ecosystem.

Pydantic v1 vs v2?

Pydantic v2 (2023+) has a Rust core and is 5–50x faster than v1. The generated models use from __future__ import annotations for forward references. Toggle off for v1-compatible output.

How are null fields handled?

Fields that are null in the JSON become Optional[Any] = None. Replace Any with the actual expected type once you know it — e.g., Optional[str] = None for a nullable string.

What formats are detected automatically?

ISO datetime strings → datetime type, UUID strings → UUID type, email addresses → EmailStr type. The appropriate imports (from datetime import datetime, from uuid import UUID, EmailStr) are added automatically.

How do nested objects work?

Nested JSON objects become nested Pydantic models. The model name is derived from the key name (capitalized). Dependencies are emitted first so the file is valid Python from top to bottom.

Can I use this with FastAPI?

Yes — FastAPI uses Pydantic natively. Paste the generated models directly into your FastAPI app. Use them as request body types: async def create_user(user: User): ...