Extra Models

Continuing with the previous example, it will be common to have more than one related model.

This is especially the case for user models, because:

• The input model needs to be able to have a password.
• The output model should not have a password.
• The database model would probably need to have a hashed password.

Danger

Never store user’s plaintext passwords. Always store a “secure hash” that you can then verify.

If you don’t know, you will learn what a “password hash” is in the security chapters.

Multiple models¶

Here's a general idea of how the models could look like with their password fields and the places where they are used:Python 3.10+Python 3.8+from fastapi import FastAPI from pydantic import BaseModel, EmailStr app = FastAPI() class UserIn(BaseModel): username: str password: str email: EmailStr full_name: str | None = None class UserOut(BaseModel): username: str email: EmailStr full_name: str | None = None class UserInDB(BaseModel): username: str hashed_password: str email: EmailStr full_name: str | None = None def fake_password_hasher(raw_password: str): return "supersecret" + raw_password def fake_save_user(user_in: UserIn): hashed_password = fake_password_hasher(user_in.password) user_in_db = UserInDB(**user_in.dict(), hashed_password=hashed_password) print("User saved! ..not really") return user_in_db @app.post("/user/", response_model=UserOut) async def create_user(user_in: UserIn): user_saved = fake_save_user(user_in) return user_saved

Info

In Pydantic v1 the method was called .dict(), it was deprecated (but still supported) in Pydantic v2, and renamed to .model_dump().

The examples here use .dict() for compatibility with Pydantic v1, but you should use .model_dump() instead if you can use Pydantic v2.

About **user_in.dict()¶

Pydantic’s .dict()¶

user_in is a Pydantic model of class UserIn.

Pydantic models have a .dict() method that returns a dict with the model’s data.

So, if we create a Pydantic object user_in like:user_in = UserIn(username="john", password="secret", email="[email protected]")

and then we call:user_dict = user_in.dict()

we now have a dict with the data in the variable user_dict (it’s a dict instead of a Pydantic model object).

And if we call:print(user_dict)

we would get a Python dict with:{ 'username': 'john', 'password': 'secret', 'email': '[email protected]', 'full_name': None, }

Unwrapping a dict

If we take a dict like user_dict and pass it to a function (or class) with **user_dict, Python will “unwrap” it. It will pass the keys and values of the user_dict directly as key-value arguments.

So, continuing with the user_dict from above, writing:UserInDB(**user_dict)

Would result in something equivalent to:UserInDB( username="john", password="secret", email="[email protected]", full_name=None, )

Or more exactly, using user_dict directly, with whatever contents it might have in the future:UserInDB( username = user_dict["username"], password = user_dict["password"], email = user_dict["email"], full_name = user_dict["full_name"], )

A Pydantic model from the contents of another¶

As in the example above we got user_dict from user_in.dict(), this code:user_dict = user_in.dict() UserInDB(**user_dict)

would be equivalent to:UserInDB(**user_in.dict())

…because user_in.dict() is a dict, and then we make Python “unwrap” it by passing it to UserInDB prepended with **.

So, we get a Pydantic model from the data in another Pydantic model.

Unwrapping a dict and extra keywords¶

And then adding the extra keyword argument hashed_password=hashed_password, like in:UserInDB(**user_in.dict(), hashed_password=hashed_password)

…ends up being like:UserInDB( username = user_dict["username"], password = user_dict["password"], email = user_dict["email"], full_name = user_dict["full_name"], hashed_password = hashed_password, )

Warning

The supporting additional functions are just to demo a possible flow of the data, but they of course are not providing any real security.

Reduce duplication

Reducing code duplication is one of the core ideas in FastAPI.

As code duplication increments the chances of bugs, security issues, code desynchronization issues (when you update in one place but not in the others), etc.

And these models are all sharing a lot of the data and duplicating attribute names and types.

We could do better.

We can declare a UserBase model that serves as a base for our other models. And then we can make subclasses of that model that inherit its attributes (type declarations, validation, etc).

All the data conversion, validation, documentation, etc. will still work as normally.

That way, we can declare just the differences between the models (with plaintext password, with hashed_password and without password):Python 3.10+Python 3.8+from fastapi import FastAPI from pydantic import BaseModel, EmailStr app = FastAPI() class UserBase(BaseModel): username: str email: EmailStr full_name: str | None = None class UserIn(UserBase): password: str class UserOut(UserBase): pass class UserInDB(UserBase): hashed_password: str def fake_password_hasher(raw_password: str): return "supersecret" + raw_password def fake_save_user(user_in: UserIn): hashed_password = fake_password_hasher(user_in.password) user_in_db = UserInDB(**user_in.dict(), hashed_password=hashed_password) print("User saved! ..not really") return user_in_db @app.post("/user/", response_model=UserOut) async def create_user(user_in: UserIn): user_saved = fake_save_user(user_in) return user_saved

Union or anyOf¶

You can declare a response to be the Union of two types, that means, that the response would be any of the two.

It will be defined in OpenAPI with anyOf.

To do that, use the standard Python type hint typing.Union:

Note

When defining a Union, include the most specific type first, followed by the less specific type. In the example below, the more specific PlaneItem comes before CarItem in Union[PlaneItem, CarItem].Python 3.10+Python 3.8+from typing import Union from fastapi import FastAPI from pydantic import BaseModel app = FastAPI() class BaseItem(BaseModel): description: str type: str class CarItem(BaseItem): type: str = "car" class PlaneItem(BaseItem): type: str = "plane" size: int items = { "item1": {"description": "All my friends drive a low rider", "type": "car"}, "item2": { "description": "Music is my aeroplane, it's my aeroplane", "type": "plane", "size": 5, }, } @app.get("/items/{item_id}", response_model=Union[PlaneItem, CarItem]) async def read_item(item_id: str): return items[item_id]

Union in Python 3.10

In this example we pass Union[PlaneItem, CarItem] as the value of the argument response_model.

Because we are passing it as a value to an argument instead of putting it in a type annotation, we have to use Union even in Python 3.10.

If it was in a type annotation we could have used the vertical bar, as:some_variable: PlaneItem | CarItem

But if we put that in response_model=PlaneItem | CarItem we would get an error, because Python would try to perform an invalid operation between PlaneItem and CarItem instead of interpreting that as a type annotation.

List of models¶

The same way, you can declare responses of lists of objects.

For that, use the standard Python typing.List (or just list in Python 3.9 and above):Python 3.9+Python 3.8+from fastapi import FastAPI from pydantic import BaseModel app = FastAPI() class Item(BaseModel): name: str description: str items = [ {"name": "Foo", "description": "There comes my hero"}, {"name": "Red", "description": "It's my aeroplane"}, ] @app.get("/items/", response_model=list[Item]) async def read_items(): return items

Response with arbitrary dict¶

You can also declare a response using a plain arbitrary dict, declaring just the type of the keys and values, without using a Pydantic model.

This is useful if you don’t know the valid field/attribute names (that would be needed for a Pydantic model) beforehand.

In this case, you can use typing.Dict (or just dict in Python 3.9 and above):Python 3.9+Python 3.8+from fastapi import FastAPI app = FastAPI() @app.get("/keyword-weights/", response_model=dict[str, float]) async def read_keyword_weights(): return {"foo": 2.3, "bar": 3.4}