typeddict

TypedDict

Use TypedDict replace pydantic definitions.

Why?

from pydantic import BaseModel


class User(BaseModel):
    name: str
    age: int = Field(default=0, ge=0)
    email: Optional[str]


user: User = {"name": "John", "age": 30}  # Type check, error!
print(repr(user))

In index.py or other framework, maybe you write the following code. And then got an type check error in Annotated[Message, ...], because the type of {"message": "..."} is not Message.

class Message(BaseModel):
    message: str


@routes.http.post("/user")
async def create_user(
    ...
) -> Annotated[Message, JSONResponse[200, {}, Message]]:
    ...
    return {"message": "Created successfully!"}

Usage

Use Annotated to provide extra information to pydantic.Field. Other than that, everything conforms to the general usage of TypedDict. Using to_pydantic will create a semantically equivalent pydantic model. You can use it in frameworks like index.py / fastapi / xpresso.

from typing_extensions import Annotated, NotRequired, TypedDict

import typeddict
from typeddict import Extra, Metadata


class User(TypedDict):
    name: str
    age: Annotated[int, Metadata(default=0), Extra(ge=0)]
    email: NotRequired[Annotated[str, Extra(min_length=5, max_length=100)]]


class Book(TypedDict):
    author: NotRequired[User]


user: User = {"name": "John", "age": 30}  # Type check, pass!
print(repr(user))

# Then use it in fastapi / index.py or other frameworks
UserModel = typeddict.to_pydantic(User)
print(repr(UserModel.__signature__))
print(repr(UserModel.parse_obj(user)))

book: Book = {"author": user}  # Type check, pass!
print(repr(book))

# Then use it in fastapi / index.py or other frameworks
BookModel = typeddict.to_pydantic(Book)
print(repr(BookModel.__signature__))
print(repr(BookModel.parse_obj(book)))

cast

Sometimes you may not need a pydantic model, you can directly use typeddict to parse the data.

import typeddict


class User(TypedDict):
    name: str
    age: Annotated[int, Metadata(default=0), Extra(ge=0)]
    email: NotRequired[Annotated[str, Extra(min_length=5, max_length=100)]]


user = typeddict.cast(User, {"name": "John", "age": 30, "unused-info": "....."})
print(repr(user))