How to use the JSON → Pydantic v2 Model
1. Paste a JSON object or API response into the input panel.
2. Set the model class name — typically singular and PascalCase (User, Product, Order).
3. The Pydantic v2 BaseModel class is generated instantly — copy it into your FastAPI or Python project.
Tip: values are used to infer smart types — ISO datetime strings become datetime, email strings become EmailStr, UUIDs become UUID, null values become Optional[...].
What is it?
Pydantic v2 is the standard data validation library for Python, used by FastAPI, Django Ninja, LangChain, and nearly every modern Python API framework. BaseModel classes validate data at runtime using Python type annotations and provide automatic JSON serialisation.
This tool generates Pydantic v2-compatible models from any JSON sample. Type inference is smart: ISO timestamp strings become datetime, date strings become date, email strings become EmailStr, and UUID strings become UUID. Null values become Optional[T] = None. Nested objects become nested BaseModel subclasses, ordered correctly so dependencies are defined first.
All imports are generated automatically based on the types used, so you can paste the output directly into your code without any editing.
Example
{"id": 1, "email": "[email protected]", "score": 9.5, "tags": ["dev"]}from pydantic import BaseModel, EmailStr
from typing import List
class UserModel(BaseModel):
id: int
email: EmailStr
score: float
tags: List[str]Use model.model_validate(data) to parse dicts, or model.model_validate_json(json_str) for JSON strings in Pydantic v2.
Benefits
Generate accurate code from your actual data
TypeScript interfaces, Python dataclasses, and OpenAI function schemas are generated directly from your real JSON — accurate field names, correct types, no guesswork.
Control LLM API costs before you scale
Token counts and per-call cost estimates for GPT-4o, Claude 3.5, and Gemini let you optimise prompt size and model choice before a feature goes to production.
Ready-to-run code and configs, not boilerplate
Every tool produces output you can paste directly into your project — API request code, prompt templates, chunk configurations — adapt rather than write from scratch.
Purpose-built for AI engineering workflows
From RAG chunking strategy to prompt template selection to function calling schema design — tools built for the specific problems AI engineers encounter daily.
Frequently asked questions
How do I use the generated model with FastAPI?
Use it directly as a request body type: @app.post('/users') async def create_user(user: UserModel): ... FastAPI validates the request body against the model automatically and generates OpenAPI documentation.
What is the difference between Pydantic v1 and v2?
Pydantic v2 (pydantic>=2.0) is ~17x faster and uses a new Rust-based validator. Breaking changes: .dict() → .model_dump(), .json() → .model_dump_json(), parse_obj() → model_validate(). This tool generates v2-compatible code.
How do I make a field optional with a default?
Use Optional[str] = None for nullable fields (already generated for null values), or use Optional[str] = 'default' for fields with a non-None default. For more control use Field(default=None, description='...').
Does this work with LangChain structured output?
Yes — LangChain's with_structured_output() method accepts Pydantic BaseModel classes directly: chain.with_structured_output(UserModel). This tool is great for generating models for LLM output parsing.
How do I validate data with the generated model?
In Pydantic v2: UserModel.model_validate({'id': 1, 'name': 'Alice'}) — raises ValidationError if invalid. Use model_validate_json(json_string) for JSON strings. Use model_validate(obj, strict=True) for strict type checking.