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Online JSON to Python Pydantic v2 Model Generator

Convert any JSON structure into strict Python Pydantic v2 `BaseModel` classes or Python `TypedDict` definitions. Ideal for FastAPI endpoints, LLM structured outputs (LangChain / LlamaIndex), and data pipelines. All code synthesis executes client-side in an isolated Web Worker, ensuring zero data leakage.

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#How to Use This Tool

1Paste your JSON data into the left input editor.
2Choose Pydantic v2 or TypedDict style.
3Customize the Root model name as needed.
4Copy the generated Python code or share a stateless Mode B link.

#Mathematical Formula & Standards

The AST engine maps JSON types into Python type hints: strings map to `str`, whole numbers to `int`, decimals to `float`, booleans to `bool`, arrays to `List[T]`, and nested objects to child `BaseModel` classes. Non-standard or hyphenated field names are mapped to valid Python identifiers with `Field(alias="...")` and `ConfigDict(populate_by_name=True)`.

#Edge Cases & Technical Considerations

Pydantic v2 vs. v1 Syntax Differences

Pydantic v2 uses `model_config = ConfigDict(...)` and modern Python 3.10+ union types where appropriate, rather than the legacy v1 inner `class Config:`.

Python Reserved Keyword Collisions

If a JSON key conflicts with a Python reserved keyword (such as `from`, `def`, `class`, `global`), the engine appends an underscore (e.g. `from_`) and binds the original key via `Field(alias="from")`.

#Frequently Asked Questions

Q:Is Pydantic v2 supported?

Yes! The generator emits modern Pydantic v2 models adhering to current FastAPI and Pydantic best practices.

Q:Can I generate Python TypedDict instead?

Yes. You can switch the Python style selector to "TypedDict" to generate standard library `typing.TypedDict` models without external dependencies.

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