Convert your Pydantic models to TypedDict with ease! 🎉
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:
- Define your models in Pydantic (with all its validation goodness) 👍
- Convert them to TypedDict when needed (for APIs like Gemini) 🔄
- Enjoy the benefits of both! 🎊
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)
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)
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
- Converts simple and complex Pydantic models 🏗
- Handles nested models, lists, and dictionaries 🔄
- Supports optional fields and unions 🤝
- Preserves type hints for better static analysis 🔍
Contributions are welcome! Feel free to open issues or submit pull requests. 🙌
This project is licensed under the MIT License. See the LICENSE file for details.
Happy coding! 🎈🎊