Body - Fields¶

The same way you can declare additional validation and metadata in path operation function parameters with Query, Path and Body, you can declare validation and metadata inside of Pydantic models using Pydantic's Field.

Import `Field`¶

First, you have to import it:

Python 3.6 and abovePython 3.10 and above

from typing import Optional

from fastapi import Body, FastAPI
from pydantic import BaseModel, Field

app = FastAPI()


class Item(BaseModel):
    name: str
    description: Optional[str] = Field(
        None, title="The description of the item", max_length=300
    )
    price: float = Field(..., gt=0, description="The price must be greater than zero")
    tax: Optional[float] = None


@app.put("/items/{item_id}")
async def update_item(item_id: int, item: Item = Body(..., embed=True)):
    results = {"item_id": item_id, "item": item}
    return results

from fastapi import Body, FastAPI
from pydantic import BaseModel, Field

app = FastAPI()


class Item(BaseModel):
    name: str
    description: str | None = Field(
        None, title="The description of the item", max_length=300
    )
    price: float = Field(..., gt=0, description="The price must be greater than zero")
    tax: float | None = None


@app.put("/items/{item_id}")
async def update_item(item_id: int, item: Item = Body(..., embed=True)):
    results = {"item_id": item_id, "item": item}
    return results

Warning

Notice that Field is imported directly from pydantic, not from fastapi as are all the rest (Query, Path, Body, etc).

Declare model attributes¶

You can then use Field with model attributes:

Python 3.6 and abovePython 3.10 and above

from typing import Optional

from fastapi import Body, FastAPI
from pydantic import BaseModel, Field

app = FastAPI()


class Item(BaseModel):
    name: str
    description: Optional[str] = Field(
        None, title="The description of the item", max_length=300
    )
    price: float = Field(..., gt=0, description="The price must be greater than zero")
    tax: Optional[float] = None


@app.put("/items/{item_id}")
async def update_item(item_id: int, item: Item = Body(..., embed=True)):
    results = {"item_id": item_id, "item": item}
    return results

from fastapi import Body, FastAPI
from pydantic import BaseModel, Field

app = FastAPI()


class Item(BaseModel):
    name: str
    description: str | None = Field(
        None, title="The description of the item", max_length=300
    )
    price: float = Field(..., gt=0, description="The price must be greater than zero")
    tax: float | None = None


@app.put("/items/{item_id}")
async def update_item(item_id: int, item: Item = Body(..., embed=True)):
    results = {"item_id": item_id, "item": item}
    return results

Field works the same way as Query, Path and Body, it has all the same parameters, etc.

Technical Details

Actually, Query, Path and others you'll see next create objects of subclasses of a common Param class, which is itself a subclass of Pydantic's FieldInfo class.

And Pydantic's Field returns an instance of FieldInfo as well.

Body also returns objects of a subclass of FieldInfo directly. And there are others you will see later that are subclasses of the Body class.

Remember that when you import Query, Path, and others from fastapi, those are actually functions that return special classes.

Tip

Notice how each model's attribute with a type, default value and Field has the same structure as a path operation function's parameter, with Field instead of Path, Query and Body.

Add extra information¶

You can declare extra information in Field, Query, Body, etc. And it will be included in the generated JSON Schema.

You will learn more about adding extra information later in the docs, when learning to declare examples.

Recap¶

You can use Pydantic's Field to declare extra validations and metadata for model attributes.

You can also use the extra keyword arguments to pass additional JSON Schema metadata.