Nested Models

With FastAPI, you can define, validate, document, and use arbitrarily deeply nested models (thanks to Pydantic).

List fields¶

You can define an attribute to be a subtype. For example, a Python list:Python 3.10+Python 3.8+from fastapi import FastAPI from pydantic import BaseModel app = FastAPI() class Item(BaseModel): name: str description: str | None = None price: float tax: float | None = None tags: list = [] @app.put("/items/{item_id}") async def update_item(item_id: int, item: Item): results = {"item_id": item_id, "item": item} return results

This will make tags be a list, although it doesn’t declare the type of the elements of the list.

List fields with type parameter¶

But Python has a specific way to declare lists with internal types, or “type parameters”:

Import typing’s List¶

In Python 3.9 and above you can use the standard list to declare these type annotations as we’ll see below. 💡

But in Python versions before 3.9 (3.6 and above), you first need to import List from standard Python's typing module:from typing import List, Union from fastapi import FastAPI from pydantic import BaseModel app = FastAPI() class Item(BaseModel): name: str description: Union[str, None] = None price: float tax: Union[float, None] = None tags: List[str] = [] @app.put("/items/{item_id}") async def update_item(item_id: int, item: Item): results = {"item_id": item_id, "item": item} return results

Declare a list with a type parameter¶

To declare types that have type parameters (internal types), like list, dict, tuple:

• If you are in a Python version lower than 3.9, import their equivalent version from the typing module
• Pass the internal type(s) as “type parameters” using square brackets: [ and ]

In Python 3.9 it would be:my_list: list[str]

In versions of Python before 3.9, it would be:from typing import List my_list: List[str]

That’s all standard Python syntax for type declarations.

Use that same standard syntax for model attributes with internal types.

So, in our example, we can make tags be specifically a "list of strings":Python 3.10+Python 3.9+Python 3.8+from fastapi import FastAPI from pydantic import BaseModel app = FastAPI() class Item(BaseModel): name: str description: str | None = None price: float tax: float | None = None tags: list[str] = [] @app.put("/items/{item_id}") async def update_item(item_id: int, item: Item): results = {"item_id": item_id, "item": item} return results

Set types¶

But then we think about it, and realize that tags shouldn’t repeat, they would probably be unique strings.

And Python has a special data type for sets of unique items, the set.

Then we can declare tags as a set of strings:Python 3.10+Python 3.9+Python 3.8+from fastapi import FastAPI from pydantic import BaseModel app = FastAPI() class Item(BaseModel): name: str description: str | None = None price: float tax: float | None = None tags: set[str] = set() @app.put("/items/{item_id}") async def update_item(item_id: int, item: Item): results = {"item_id": item_id, "item": item} return results

With this, even if you receive a request with duplicate data, it will be converted to a set of unique items.

And whenever you output that data, even if the source had duplicates, it will be output as a set of unique items.

And it will be annotated / documented accordingly too.

Nested Models¶

Each attribute of a Pydantic model has a type.

But that type can itself be another Pydantic model.

So, you can declare deeply nested JSON “objects” with specific attribute names, types and validations.

All that, arbitrarily nested.

Define a submodel¶

For example, we can define an Image model:Python 3.10+Python 3.9+Python 3.8+from fastapi import FastAPI from pydantic import BaseModel app = FastAPI() class Image(BaseModel): url: str name: str class Item(BaseModel): name: str description: str | None = None price: float tax: float | None = None tags: set[str] = set() image: Image | None = None @app.put("/items/{item_id}") async def update_item(item_id: int, item: Item): results = {"item_id": item_id, "item": item} return results

Use the submodel as a type¶

And then we can use it as the type of an attribute:Python 3.10+Python 3.9+Python 3.8+from fastapi import FastAPI from pydantic import BaseModel app = FastAPI() class Image(BaseModel): url: str name: str class Item(BaseModel): name: str description: str | None = None price: float tax: float | None = None tags: set[str] = set() image: Image | None = None @app.put("/items/{item_id}") async def update_item(item_id: int, item: Item): results = {"item_id": item_id, "item": item} return results

This would mean that FastAPI would expect a body similar to:{ "name": "Foo", "description": "The pretender", "price": 42.0, "tax": 3.2, "tags": ["rock", "metal", "bar"], "image": { "url": "http://example.com/baz.jpg", "name": "The Foo live" } }

Again, doing just that declaration, with FastAPI you get:

• Editor support (completion, etc.), even for nested models
• Data conversion
• Data validation
• Automatic documentation

Special types and validation

Apart from normal singular types like str, int, float, etc. you can use more complex singular types that inherit from str.

To see all the options you have, checkout the docs for Pydantic’s exotic types. You will see some examples in the next chapter.

For example, as in the Image model we have a url field, we can declare it to be an instance of Pydantic's HttpUrl instead of a str:Python 3.10+Python 3.9+Python 3.8+from fastapi import FastAPI from pydantic import BaseModel, HttpUrl app = FastAPI() class Image(BaseModel): url: HttpUrl name: str class Item(BaseModel): name: str description: str | None = None price: float tax: float | None = None tags: set[str] = set() image: Image | None = None @app.put("/items/{item_id}") async def update_item(item_id: int, item: Item): results = {"item_id": item_id, "item": item} return results

The string will be checked to be a valid URL, and documented in JSON Schema / OpenAPI as such.

Attributes with lists of submodels

You can also use Pydantic models as subtypes of list, set, etc.:Python 3.10+Python 3.9+Python 3.8+from fastapi import FastAPI from pydantic import BaseModel, HttpUrl app = FastAPI() class Image(BaseModel): url: HttpUrl name: str class Item(BaseModel): name: str description: str | None = None price: float tax: float | None = None tags: set[str] = set() images: list[Image] | None = None @app.put("/items/{item_id}") async def update_item(item_id: int, item: Item): results = {"item_id": item_id, "item": item} return results

This will expect (convert, validate, document, etc.) a JSON body like:{ "name": "Foo", "description": "The pretender", "price": 42.0, "tax": 3.2, "tags": [ "rock", "metal", "bar" ], "images": [ { "url": "http://example.com/baz.jpg", "name": "The Foo live" }, { "url": "http://example.com/dave.jpg", "name": "The Baz" } ] }

Info

Notice how the images key now has a list of image objects.

Deeply nested models¶

You can define arbitrarily deeply nested models:Python 3.10+Python 3.9+Python 3.8+from fastapi import FastAPI from pydantic import BaseModel, HttpUrl app = FastAPI() class Image(BaseModel): url: HttpUrl name: str class Item(BaseModel): name: str description: str | None = None price: float tax: float | None = None tags: set[str] = set() images: list[Image] | None = None class Offer(BaseModel): name: str description: str | None = None price: float items: list[Item] @app.post("/offers/") async def create_offer(offer: Offer): return offer

Info

Notice how Offer has a list of Items, which in turn have an optional list of Images

Bodies of pure lists

If the top level value of the JSON body you expect is a JSON array (a Python list), you can declare the type in the parameter of the function, the same as in Pydantic models:images: List[Image]

or in Python 3.9 and above:images: list[Image]

as in:Python 3.9+Python 3.8+from fastapi import FastAPI from pydantic import BaseModel, HttpUrl app = FastAPI() class Image(BaseModel): url: HttpUrl name: str @app.post("/images/multiple/") async def create_multiple_images(images: list[Image]): return images

Editor support everywhere

And you get editor support everywhere.

Even for items inside of lists:

You couldn’t get this kind of editor support if you were working directly with dict instead of Pydantic models.

But you don’t have to worry about them either, incoming dicts are converted automatically and your output is converted automatically to JSON too.

Bodies of arbitrary dicts¶

You can also declare a body as a dict with keys of some type and values of some other type.

This way, you don’t have to know beforehand what the valid field/attribute names are (as would be the case with Pydantic models).

This would be useful if you want to receive keys that you don’t already know.

---

Another useful case is when you want to have keys of another type (e.g., int).

That’s what we are going to see here.

In this case, you would accept any dict as long as it has int keys with float values:Python 3.9+Python 3.8+from fastapi import FastAPI app = FastAPI() @app.post("/index-weights/") async def create_index_weights(weights: dict[int, float]): return weights