🚀 PydanType: Pydantic to TypedDict Converter

Convert your Pydantic models to TypedDict with ease! 🎉

🌟 Motivation

Recently, Google Gemini introduced the ability to generate structured output, but here's the catch: unlike many environments that accept Pydantic models, they require TypeDict. It was tricky for me since I had a lot of Pydantic models in other projects, and I figured I wasn’t the only one. So, I created a simple utility that converts any Pydantic model to TypeDict, making it compatible with Gemini. Hopefully, this helps you as well! 💡

That's when this utility was born! Now you can:

1. Define your models in Pydantic (with all its validation goodness) 👍
2. Convert them to TypedDict when needed (for APIs like Gemini) 🔄
3. Enjoy the benefits of both! 🎊

🚀 Quick Start

Try it out instantly in our Colab notebook:

Install the package:

pip install pip install git+https://github.com/unclecode/pydantype.git

Use it in your code:

from pydantic import BaseModel
from pydantype import convert

class MyModel(BaseModel):
    name: str
    age: int

MyTypedDict = convert(MyModel)

🌈 Gemini 1.5 Pro Example

Here's how you can use this utility with Google's Gemini 1.5 Pro:

import google.generativeai as genai
from pydantic import BaseModel
from typing import List
from pydantype import convert

class Recipe(BaseModel):
    recipe_name: str
    ingredients: str

class RecipeList(BaseModel):
    recipes: List[Recipe]

RecipeListDict = convert(RecipeList)

model = genai.GenerativeModel('gemini-1.5-pro',
                              generation_config={
                                  "response_mime_type": "application/json",
                                  "response_schema": RecipeListDict
                              })

prompt = "List 3 popular cookie recipes"
response = model.generate_content(prompt)
print(response.text)

🎨 General Example

Here's a more general example showcasing various Pydantic features:

from typing import List, Optional
from pydantic import BaseModel, Field
from pydantype import convert

class Address(BaseModel):
    street: str
    city: str
    country: str = Field(default="Unknown")

class Person(BaseModel):
    name: str
    age: int
    address: Address
    hobbies: List[str] = []
    nickname: Optional[str] = None

PersonDict = convert(Person)

# PersonDict is now a TypedDict with the same structure as Person

🛠 Features

• Converts simple and complex Pydantic models 🏗
• Handles nested models, lists, and dictionaries 🔄
• Supports optional fields and unions 🤝
• Preserves type hints for better static analysis 🔍

🤝 Contributing

Contributions are welcome! Feel free to open issues or submit pull requests. 🙌

📜 License

This project is licensed under the MIT License. See the LICENSE file for details.

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Happy coding! 🎈🎊