Response Model - Return Type¶
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But you can help translating it: Contributing.
You can declare the type used for the response by annotating the path operation function return type.
You can use type annotations the same way you would for input data in function parameters, you can use Pydantic models, lists, dictionaries, scalar values like integers, booleans, etc.
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.post("/items/")
async def create_item(item: Item) -> Item:
return item
@app.get("/items/")
async def read_items() -> list[Item]:
return [
Item(name="Portal Gun", price=42.0),
Item(name="Plumbus", price=32.0),
]
from typing import 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.post("/items/")
async def create_item(item: Item) -> Item:
return item
@app.get("/items/")
async def read_items() -> list[Item]:
return [
Item(name="Portal Gun", price=42.0),
Item(name="Plumbus", price=32.0),
]
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.post("/items/")
async def create_item(item: Item) -> Item:
return item
@app.get("/items/")
async def read_items() -> List[Item]:
return [
Item(name="Portal Gun", price=42.0),
Item(name="Plumbus", price=32.0),
]
FastAPI will use this return type to:
- Validate the returned data.
- If the data is invalid (e.g. you are missing a field), it means that your app code is broken, not returning what it should, and it will return a server error instead of returning incorrect data. This way you and your clients can be certain that they will receive the data and the data shape expected.
- Add a JSON Schema for the response, in the OpenAPI path operation.
- This will be used by the automatic docs.
- It will also be used by automatic client code generation tools.
But most importantly:
- It will limit and filter the output data to what is defined in the return type.
- This is particularly important for security, we'll see more of that below.
response_model Parameter¶
There are some cases where you need or want to return some data that is not exactly what the type declares.
For example, you could want to return a dictionary or a database object, but declare it as a Pydantic model. This way the Pydantic model would do all the data documentation, validation, etc. for the object that you returned (e.g. a dictionary or database object).
If you added the return type annotation, tools and editors would complain with a (correct) error telling you that your function is returning a type (e.g. a dict) that is different from what you declared (e.g. a Pydantic model).
In those cases, you can use the path operation decorator parameter response_model instead of the return type.
You can use the response_model parameter in any of the path operations:
@app.get()@app.post()@app.put()@app.delete()- etc.
from typing import Any
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.post("/items/", response_model=Item)
async def create_item(item: Item) -> Any:
return item
@app.get("/items/", response_model=list[Item])
async def read_items() -> Any:
return [
{"name": "Portal Gun", "price": 42.0},
{"name": "Plumbus", "price": 32.0},
]
from typing import Any, 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.post("/items/", response_model=Item)
async def create_item(item: Item) -> Any:
return item
@app.get("/items/", response_model=list[Item])
async def read_items() -> Any:
return [
{"name": "Portal Gun", "price": 42.0},
{"name": "Plumbus", "price": 32.0},
]
from typing import Any, 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.post("/items/", response_model=Item)
async def create_item(item: Item) -> Any:
return item
@app.get("/items/", response_model=List[Item])
async def read_items() -> Any:
return [
{"name": "Portal Gun", "price": 42.0},
{"name": "Plumbus", "price": 32.0},
]
Note
Notice that response_model is a parameter of the "decorator" method (get, post, etc). Not of your path operation function, like all the parameters and body.
response_model receives the same type you would declare for a Pydantic model field, so, it can be a Pydantic model, but it can also be, e.g. a list of Pydantic models, like List[Item].
FastAPI will use this response_model to do all the data documentation, validation, etc. and also to convert and filter the output data to its type declaration.
Tip
If you have strict type checks in your editor, mypy, etc, you can declare the function return type as Any.
That way you tell the editor that you are intentionally returning anything. But FastAPI will still do the data documentation, validation, filtering, etc. with the response_model.
response_model Priority¶
If you declare both a return type and a response_model, the response_model will take priority and be used by FastAPI.
This way you can add correct type annotations to your functions even when you are returning a type different than the response model, to be used by the editor and tools like mypy. And still you can have FastAPI do the data validation, documentation, etc. using the response_model.
You can also use response_model=None to disable creating a response model for that path operation, you might need to do it if you are adding type annotations for things that are not valid Pydantic fields, you will see an example of that in one of the sections below.
Return the same input data¶
Here we are declaring a UserIn model, it will contain a plaintext password:
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
# Don't do this in production!
@app.post("/user/")
async def create_user(user: UserIn) -> UserIn:
return user
from typing import Union
from fastapi import FastAPI
from pydantic import BaseModel, EmailStr
app = FastAPI()
class UserIn(BaseModel):
username: str
password: str
email: EmailStr
full_name: Union[str, None] = None
# Don't do this in production!
@app.post("/user/")
async def create_user(user: UserIn) -> UserIn:
return user
Info
To use EmailStr, first install email_validator.
E.g. pip install email-validator
or pip install pydantic[email].
And we are using this model to declare our input and the same model to declare our output:
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
# Don't do this in production!
@app.post("/user/")
async def create_user(user: UserIn) -> UserIn:
return user
from typing import Union
from fastapi import FastAPI
from pydantic import BaseModel, EmailStr
app = FastAPI()
class UserIn(BaseModel):
username: str
password: str
email: EmailStr
full_name: Union[str, None] = None
# Don't do this in production!
@app.post("/user/")
async def create_user(user: UserIn) -> UserIn:
return user
Now, whenever a browser is creating a user with a password, the API will return the same password in the response.
In this case, it might not be a problem, because it's the same user sending the password.
But if we use the same model for another path operation, we could be sending our user's passwords to every client.
Danger
Never store the plain password of a user or send it in a response like this, unless you know all the caveats and you know what you are doing.
Add an output model¶
We can instead create an input model with the plaintext password and an output model without it:
from typing import Any
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
@app.post("/user/", response_model=UserOut)
async def create_user(user: UserIn) -> Any:
return user
from typing import Any, Union
from fastapi import FastAPI
from pydantic import BaseModel, EmailStr
app = FastAPI()
class UserIn(BaseModel):
username: str
password: str
email: EmailStr
full_name: Union[str, None] = None
class UserOut(BaseModel):
username: str
email: EmailStr
full_name: Union[str, None] = None
@app.post("/user/", response_model=UserOut)
async def create_user(user: UserIn) -> Any:
return user
Here, even though our path operation function is returning the same input user that contains the password:
from typing import Any
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
@app.post("/user/", response_model=UserOut)
async def create_user(user: UserIn) -> Any:
return user
from typing import Any, Union
from fastapi import FastAPI
from pydantic import BaseModel, EmailStr
app = FastAPI()
class UserIn(BaseModel):
username: str
password: str
email: EmailStr
full_name: Union[str, None] = None
class UserOut(BaseModel):
username: str
email: EmailStr
full_name: Union[str, None] = None
@app.post("/user/", response_model=UserOut)
async def create_user(user: UserIn) -> Any:
return user
...we declared the response_model to be our model UserOut, that doesn't include the password:
from typing import Any
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
@app.post("/user/", response_model=UserOut)
async def create_user(user: UserIn) -> Any:
return user
from typing import Any, Union
from fastapi import FastAPI
from pydantic import BaseModel, EmailStr
app = FastAPI()
class UserIn(BaseModel):
username: str
password: str
email: EmailStr
full_name: Union[str, None] = None
class UserOut(BaseModel):
username: str
email: EmailStr
full_name: Union[str, None] = None
@app.post("/user/", response_model=UserOut)
async def create_user(user: UserIn) -> Any:
return user
So, FastAPI will take care of filtering out all the data that is not declared in the output model (using Pydantic).
response_model or Return Type¶
In this case, because the two models are different, if we annotated the function return type as UserOut, the editor and tools would complain that we are returning an invalid type, as those are different classes.
That's why in this example we have to declare it in the response_model parameter.
...but continue reading below to see how to overcome that.
Return Type and Data Filtering¶
Let's continue from the previous example. We wanted to annotate the function with one type but return something that includes more data.
We want FastAPI to keep filtering the data using the response model.
In the previous example, because the classes were different, we had to use the response_model parameter. But that also means that we don't get the support from the editor and tools checking the function return type.
But in most of the cases where we need to do something like this, we want the model just to filter/remove some of the data as in this example.
And in those cases, we can use classes and inheritance to take advantage of function type annotations to get better support in the editor and tools, and still get the FastAPI data filtering.
from fastapi import FastAPI
from pydantic import BaseModel, EmailStr
app = FastAPI()
class BaseUser(BaseModel):
username: str
email: EmailStr
full_name: str | None = None
class UserIn(BaseUser):
password: str
@app.post("/user/")
async def create_user(user: UserIn) -> BaseUser:
return user
from typing import Union
from fastapi import FastAPI
from pydantic import BaseModel, EmailStr
app = FastAPI()
class BaseUser(BaseModel):
username: str
email: EmailStr
full_name: Union[str, None] = None
class UserIn(BaseUser):
password: str
@app.post("/user/")
async def create_user(user: UserIn) -> BaseUser:
return user
With this, we get tooling support, from editors and mypy as this code is correct in terms of types, but we also get the data filtering from FastAPI.
How does this work? Let's check that out. 🤓
Type Annotations and Tooling¶
First let's see how editors, mypy and other tools would see this.
BaseUser has the base fields. Then UserIn inherits from BaseUser and adds the password field, so, it will include all the fields from both models.
We annotate the function return type as BaseUser, but we are actually returning a UserIn instance.
The editor, mypy, and other tools won't complain about this because, in typing terms, UserIn is a subclass of BaseUser, which means it's a valid type when what is expected is anything that is a BaseUser.
FastAPI Data Filtering¶
Now, for FastAPI, it will see the return type and make sure that what you return includes only the fields that are declared in the type.
FastAPI does several things internally with Pydantic to make sure that those same rules of class inheritance are not used for the returned data filtering, otherwise you could end up returning much more data than what you expected.
This way, you can get the best of both worlds: type annotations with tooling support and data filtering.
See it in the docs¶
When you see the automatic docs, you can check that the input model and output model will both have their own JSON Schema:
And both models will be used for the interactive API documentation:
Other Return Type Annotations¶
There might be cases where you return something that is not a valid Pydantic field and you annotate it in the function, only to get the support provided by tooling (the editor, mypy, etc).
Return a Response Directly¶
The most common case would be returning a Response directly as explained later in the advanced docs.
from fastapi import FastAPI, Response
from fastapi.responses import JSONResponse, RedirectResponse
app = FastAPI()
@app.get("/portal")
async def get_portal(teleport: bool = False) -> Response:
if teleport:
return RedirectResponse(url="https://www.youtube.com/watch?v=dQw4w9WgXcQ")
return JSONResponse(content={"message": "Here's your interdimensional portal."})
This simple case is handled automatically by FastAPI because the return type annotation is the class (or a subclass) of Response.
And tools will also be happy because both RedirectResponse and JSONResponse are subclasses of Response, so the type annotation is correct.
Annotate a Response Subclass¶
You can also use a subclass of Response in the type annotation:
from fastapi import FastAPI
from fastapi.responses import RedirectResponse
app = FastAPI()
@app.get("/teleport")
async def get_teleport() -> RedirectResponse:
return RedirectResponse(url="https://www.youtube.com/watch?v=dQw4w9WgXcQ")
This will also work because RedirectResponse is a subclass of Response, and FastAPI will automatically handle this simple case.
Invalid Return Type Annotations¶
But when you return some other arbitrary object that is not a valid Pydantic type (e.g. a database object) and you annotate it like that in the function, FastAPI will try to create a Pydantic response model from that type annotation, and will fail.
The same would happen if you had something like a union between different types where one or more of them are not valid Pydantic types, for example this would fail 💥:
from fastapi import FastAPI, Response
from fastapi.responses import RedirectResponse
app = FastAPI()
@app.get("/portal")
async def get_portal(teleport: bool = False) -> Response | dict:
if teleport:
return RedirectResponse(url="https://www.youtube.com/watch?v=dQw4w9WgXcQ")
return {"message": "Here's your interdimensional portal."}
from typing import Union
from fastapi import FastAPI, Response
from fastapi.responses import RedirectResponse
app = FastAPI()
@app.get("/portal")
async def get_portal(teleport: bool = False) -> Union[Response, dict]:
if teleport:
return RedirectResponse(url="https://www.youtube.com/watch?v=dQw4w9WgXcQ")
return {"message": "Here's your interdimensional portal."}
...this fails because the type annotation is not a Pydantic type and is not just a single Response class or subclass, it's a union (any of the two) between a Response and a dict.
Disable Response Model¶
Continuing from the example above, you might not want to have the default data validation, documentation, filtering, etc. that is performed by FastAPI.
But you might want to still keep the return type annotation in the function to get the support from tools like editors and type checkers (e.g. mypy).
In this case, you can disable the response model generation by setting response_model=None:
from fastapi import FastAPI, Response
from fastapi.responses import RedirectResponse
app = FastAPI()
@app.get("/portal", response_model=None)
async def get_portal(teleport: bool = False) -> Response | dict:
if teleport:
return RedirectResponse(url="https://www.youtube.com/watch?v=dQw4w9WgXcQ")
return {"message": "Here's your interdimensional portal."}
from typing import Union
from fastapi import FastAPI, Response
from fastapi.responses import RedirectResponse
app = FastAPI()
@app.get("/portal", response_model=None)
async def get_portal(teleport: bool = False) -> Union[Response, dict]:
if teleport:
return RedirectResponse(url="https://www.youtube.com/watch?v=dQw4w9WgXcQ")
return {"message": "Here's your interdimensional portal."}
This will make FastAPI skip the response model generation and that way you can have any return type annotations you need without it affecting your FastAPI application. 🤓
Response Model encoding parameters¶
Your response model could have default values, like:
from fastapi import FastAPI
from pydantic import BaseModel
app = FastAPI()
class Item(BaseModel):
name: str
description: str | None = None
price: float
tax: float = 10.5
tags: list[str] = []
items = {
"foo": {"name": "Foo", "price": 50.2},
"bar": {"name": "Bar", "description": "The bartenders", "price": 62, "tax": 20.2},
"baz": {"name": "Baz", "description": None, "price": 50.2, "tax": 10.5, "tags": []},
}
@app.get("/items/{item_id}", response_model=Item, response_model_exclude_unset=True)
async def read_item(item_id: str):
return items[item_id]
from typing import Union
from fastapi import FastAPI
from pydantic import BaseModel
app = FastAPI()
class Item(BaseModel):
name: str
description: Union[str, None] = None
price: float
tax: float = 10.5
tags: list[str] = []
items = {
"foo": {"name": "Foo", "price": 50.2},
"bar": {"name": "Bar", "description": "The bartenders", "price": 62, "tax": 20.2},
"baz": {"name": "Baz", "description": None, "price": 50.2, "tax": 10.5, "tags": []},
}
@app.get("/items/{item_id}", response_model=Item, response_model_exclude_unset=True)
async def read_item(item_id: str):
return items[item_id]
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: float = 10.5
tags: List[str] = []
items = {
"foo": {"name": "Foo", "price": 50.2},
"bar": {"name": "Bar", "description": "The bartenders", "price": 62, "tax": 20.2},
"baz": {"name": "Baz", "description": None, "price": 50.2, "tax": 10.5, "tags": []},
}
@app.get("/items/{item_id}", response_model=Item, response_model_exclude_unset=True)
async def read_item(item_id: str):
return items[item_id]
description: Union[str, None] = None(orstr | None = Nonein Python 3.10) has a default ofNone.tax: float = 10.5has a default of10.5.tags: List[str] = []as a default of an empty list:[].
but you might want to omit them from the result if they were not actually stored.
For example, if you have models with many optional attributes in a NoSQL database, but you don't want to send very long JSON responses full of default values.
Use the response_model_exclude_unset parameter¶
You can set the path operation decorator parameter response_model_exclude_unset=True:
from fastapi import FastAPI
from pydantic import BaseModel
app = FastAPI()
class Item(BaseModel):
name: str
description: str | None = None
price: float
tax: float = 10.5
tags: list[str] = []
items = {
"foo": {"name": "Foo", "price": 50.2},
"bar": {"name": "Bar", "description": "The bartenders", "price": 62, "tax": 20.2},
"baz": {"name": "Baz", "description": None, "price": 50.2, "tax": 10.5, "tags": []},
}
@app.get("/items/{item_id}", response_model=Item, response_model_exclude_unset=True)
async def read_item(item_id: str):
return items[item_id]
from typing import Union
from fastapi import FastAPI
from pydantic import BaseModel
app = FastAPI()
class Item(BaseModel):
name: str
description: Union[str, None] = None
price: float
tax: float = 10.5
tags: list[str] = []
items = {
"foo": {"name": "Foo", "price": 50.2},
"bar": {"name": "Bar", "description": "The bartenders", "price": 62, "tax": 20.2},
"baz": {"name": "Baz", "description": None, "price": 50.2, "tax": 10.5, "tags": []},
}
@app.get("/items/{item_id}", response_model=Item, response_model_exclude_unset=True)
async def read_item(item_id: str):
return items[item_id]
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: float = 10.5
tags: List[str] = []
items = {
"foo": {"name": "Foo", "price": 50.2},
"bar": {"name": "Bar", "description": "The bartenders", "price": 62, "tax": 20.2},
"baz": {"name": "Baz", "description": None, "price": 50.2, "tax": 10.5, "tags": []},
}
@app.get("/items/{item_id}", response_model=Item, response_model_exclude_unset=True)
async def read_item(item_id: str):
return items[item_id]
and those default values won't be included in the response, only the values actually set.
So, if you send a request to that path operation for the item with ID foo, the response (not including default values) will be:
{
"name": "Foo",
"price": 50.2
}
Info
FastAPI uses Pydantic model's .dict() with its exclude_unset parameter to achieve this.
Info
You can also use:
response_model_exclude_defaults=Trueresponse_model_exclude_none=True
as described in the Pydantic docs for exclude_defaults and exclude_none.
Data with values for fields with defaults¶
But if your data has values for the model's fields with default values, like the item with ID bar:
{
"name": "Bar",
"description": "The bartenders",
"price": 62,
"tax": 20.2
}
they will be included in the response.
Data with the same values as the defaults¶
If the data has the same values as the default ones, like the item with ID baz:
{
"name": "Baz",
"description": None,
"price": 50.2,
"tax": 10.5,
"tags": []
}
FastAPI is smart enough (actually, Pydantic is smart enough) to realize that, even though description, tax, and tags have the same values as the defaults, they were set explicitly (instead of taken from the defaults).
So, they will be included in the JSON response.
Tip
Notice that the default values can be anything, not only None.
They can be a list ([]), a float of 10.5, etc.
response_model_include and response_model_exclude¶
You can also use the path operation decorator parameters response_model_include and response_model_exclude.
They take a set of str with the name of the attributes to include (omitting the rest) or to exclude (including the rest).
This can be used as a quick shortcut if you have only one Pydantic model and want to remove some data from the output.
Tip
But it is still recommended to use the ideas above, using multiple classes, instead of these parameters.
This is because the JSON Schema generated in your app's OpenAPI (and the docs) will still be the one for the complete model, even if you use response_model_include or response_model_exclude to omit some attributes.
This also applies to response_model_by_alias that works similarly.
from fastapi import FastAPI
from pydantic import BaseModel
app = FastAPI()
class Item(BaseModel):
name: str
description: str | None = None
price: float
tax: float = 10.5
items = {
"foo": {"name": "Foo", "price": 50.2},
"bar": {"name": "Bar", "description": "The Bar fighters", "price": 62, "tax": 20.2},
"baz": {
"name": "Baz",
"description": "There goes my baz",
"price": 50.2,
"tax": 10.5,
},
}
@app.get(
"/items/{item_id}/name",
response_model=Item,
response_model_include={"name", "description"},
)
async def read_item_name(item_id: str):
return items[item_id]
@app.get("/items/{item_id}/public", response_model=Item, response_model_exclude={"tax"})
async def read_item_public_data(item_id: str):
return items[item_id]
from typing import Union
from fastapi import FastAPI
from pydantic import BaseModel
app = FastAPI()
class Item(BaseModel):
name: str
description: Union[str, None] = None
price: float
tax: float = 10.5
items = {
"foo": {"name": "Foo", "price": 50.2},
"bar": {"name": "Bar", "description": "The Bar fighters", "price": 62, "tax": 20.2},
"baz": {
"name": "Baz",
"description": "There goes my baz",
"price": 50.2,
"tax": 10.5,
},
}
@app.get(
"/items/{item_id}/name",
response_model=Item,
response_model_include={"name", "description"},
)
async def read_item_name(item_id: str):
return items[item_id]
@app.get("/items/{item_id}/public", response_model=Item, response_model_exclude={"tax"})
async def read_item_public_data(item_id: str):
return items[item_id]
Tip
The syntax {"name", "description"} creates a set with those two values.
It is equivalent to set(["name", "description"]).
Using lists instead of sets¶
If you forget to use a set and use a list or tuple instead, FastAPI will still convert it to a set and it will work correctly:
from fastapi import FastAPI
from pydantic import BaseModel
app = FastAPI()
class Item(BaseModel):
name: str
description: str | None = None
price: float
tax: float = 10.5
items = {
"foo": {"name": "Foo", "price": 50.2},
"bar": {"name": "Bar", "description": "The Bar fighters", "price": 62, "tax": 20.2},
"baz": {
"name": "Baz",
"description": "There goes my baz",
"price": 50.2,
"tax": 10.5,
},
}
@app.get(
"/items/{item_id}/name",
response_model=Item,
response_model_include=["name", "description"],
)
async def read_item_name(item_id: str):
return items[item_id]
@app.get("/items/{item_id}/public", response_model=Item, response_model_exclude=["tax"])
async def read_item_public_data(item_id: str):
return items[item_id]
from typing import Union
from fastapi import FastAPI
from pydantic import BaseModel
app = FastAPI()
class Item(BaseModel):
name: str
description: Union[str, None] = None
price: float
tax: float = 10.5
items = {
"foo": {"name": "Foo", "price": 50.2},
"bar": {"name": "Bar", "description": "The Bar fighters", "price": 62, "tax": 20.2},
"baz": {
"name": "Baz",
"description": "There goes my baz",
"price": 50.2,
"tax": 10.5,
},
}
@app.get(
"/items/{item_id}/name",
response_model=Item,
response_model_include=["name", "description"],
)
async def read_item_name(item_id: str):
return items[item_id]
@app.get("/items/{item_id}/public", response_model=Item, response_model_exclude=["tax"])
async def read_item_public_data(item_id: str):
return items[item_id]
Recap¶
Use the path operation decorator's parameter response_model to define response models and especially to ensure private data is filtered out.
Use response_model_exclude_unset to return only the values explicitly set.