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Request

You can use ormar Models in fastapi request Body parameters instead of pydantic models.

You can of course also mix ormar.Models with pydantic ones if you need to.

One of the most common tasks in requests is excluding certain fields that you do not want to include in the payload you send to API.

This can be achieved in several ways in ormar so below you can review your options and select the one most suitable for your situation.

Excluding fields in request

Optional fields

Note that each field that is optional is not required, that means that Optional fields can be skipped both in response and in requests.

Field is not required if (any/many/all) of following:

  • Field is marked with nullable=True
  • Field has default value or function provided, i.e. default="Test"
  • Field has a server_default value set
  • Field is an autoincrement=True primary_key field (note that ormar.Integer primary_key is autoincrement by default)

Example:

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class User(ormar.Model):
    class Meta:
        tablename: str = "users"
        metadata = metadata
        database = database

    id: int = ormar.Integer(primary_key=True)
    email: str = ormar.String(max_length=255)
    password: str = ormar.String(max_length=255)
    first_name: str = ormar.String(max_length=255, nullable=True)
    last_name: str = ormar.String(max_length=255)
    category: str = ormar.String(max_length=255, default="User")

In above example fields id (is an autoincrement Integer), first_name ( has nullable=True) and category (has default) are optional and can be skipped in response and model wil still validate.

If the field is nullable you don't have to include it in payload during creation as well as in response, so given example above you can:

Warning

Note that although you do not have to pass the optional field, you still can do it. And if someone will pass a value it will be used later unless you take measures to prevent it.

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# note that app is an FastApi app
@app.post("/users/", response_model=User) # here we use ormar.Model in response
async def create_user(user: User):  # here we use ormar.Model in request parameter
    return await user.save()

That means that if you do not pass i.e. first_name in request it will validate correctly (as field is optional), None will be saved in the database.

Generate pydantic model from ormar.Model

Since task of excluding fields is so common ormar has a special way to generate pydantic models from existing ormar.Models without you needing to retype all the fields.

That method is get_pydantic() method available on all models classes.

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# generate a tree of models without password on User and without priority on nested Category
RequestUser = User.get_pydantic(exclude={"password": ..., "category": {"priority"}})
@app.post("/users3/", response_model=User) # here you can also use both ormar/pydantic
async def create_user3(user: RequestUser):  # use the generated model here
    # note how now user is pydantic and not ormar Model so you need to convert
    return await User(**user.dict()).save()

Note

To see more examples and read more visit get_pydantic part of the documentation.

Warning

The get_pydantic method generates all models in a tree of nested models according to an algorithm that allows to avoid loops in models (same algorithm that is used in dict(), select_all() etc.)

That means that nested models won't have reference to parent model (by default ormar relation is biderectional).

Note also that if given model exists in a tree more than once it will be doubled in pydantic models (each occurance will have separate own model). That way you can exclude/include different fields on different leafs of the tree.

Mypy and type checking

Note that assigning a function as a python type passes at runtime (as it's not checked) the static type checkers like mypy will complain.

Although result of the function call will always be the same for given model using a dynamically created type is not allowed.

Therefore you have two options:

First one is to simply add # type: ignore to skip the type checking

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RequestUser = User.get_pydantic(exclude={"password": ..., "category": {"priority"}})
@app.post("/users3/", response_model=User)
async def create_user3(user: RequestUser):  # type: ignore
    # note how now user is not ormar Model so you need to convert
    return await User(**user.dict()).save()

The second one is a little bit more hacky and utilizes a way in which fastapi extract function parameters.

You can overwrite the __annotations__ entry for given param.

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RequestUser = User.get_pydantic(exclude={"password": ..., "category": {"priority"}})
# do not use the app decorator
async def create_user3(user: User):  # use ormar model here
    return await User(**user.dict()).save()
# overwrite the function annotations entry for user param with generated model 
create_user3.__annotations__["user"] = RequestUser
# manually call app functions (app.get, app.post etc.) and pass your function reference
app.post("/categories/", response_model=User)(create_user3)

Note that this will cause mypy to "think" that user is an ormar model but since in request it doesn't matter that much (you pass jsonized dict anyway and you need to convert before saving).

That still should work fine as generated model will be a subset of fields, so all needed fields will validate, and all not used fields will fail at runtime.

Separate pydantic model

The final solution is to just create separate pydantic model manually. That works exactly the same as with normal fastapi application, so you can have different models for response and requests etc.

Sample:

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import pydantic

class UserCreate(pydantic.BaseModel):
    class Config:
        orm_mode = True

    email: str
    first_name: str
    last_name: str
    password: str


@app.post("/users3/", response_model=User) # use ormar model here (but of course you CAN use pydantic also here)
async def create_user3(user: UserCreate):  # use pydantic model here
    # note how now request param is a pydantic model and not the ormar one
    # so you need to parse/convert it to ormar before you can use database
    return await User(**user.dict()).save()