Pydantic support

Strawberry comes with support for Pydantic. This allows for the creation of Strawberry types from pydantic models without having to write code twice.

Here's a basic example of how this works, let's say we have a pydantic Model for a user, like this:

from datetime import datetime
from typing import List, Optional
from pydantic import BaseModel


class User(BaseModel):
    id: int
    name: str
    signup_ts: Optional[datetime] = None
    friends: List[int] = []

We can create a Strawberry type by using the strawberry.experimental.pydantic.type decorator:

import strawberry

from .models import User

@strawberry.experimental.pydantic.type(model=User)
class UserType:
    id: strawberry.auto
    name: strawberry.auto
    friends: strawberry.auto

The strawberry.experimental.pydantic.type decorator accepts a Pydantic model and wraps a class that contains dataclass style fields with strawberry.auto as the type annotation. The fields marked with strawberry.auto will inherit their types from the Pydantic model.

If you want to include all of the fields from your Pydantic model, you can instead pass all_fields=True to the decorator.

-> Note Care should be taken to avoid accidentally exposing fields that -> weren't meant to be exposed on an API using this feature.

import strawberry

from .models import User

@strawberry.experimental.pydantic.type(model=User, all_fields=True)
class UserType:
    pass

Input types

Input types are similar to types; we can create one by using the strawberry.experimental.pydantic.input decorator:

import strawberry

from .models import User

@strawberry.experimental.pydantic.input(model=User)
class UserInput:
    id: strawberry.auto
    name: strawberry.auto
    friends: strawberry.auto

Interface types

Interface types are similar to normal types; we can create one by using the strawberry.experimental.pydantic.interface decorator:

import strawberry
from pydantic import BaseModel
from typing import List

# pydantic types
class User(BaseModel):
    id: int
    name: str

class NormalUser(User):
    friends: List[int] = []

class AdminUser(User):
    role: int

# strawberry types
@strawberry.experimental.pydantic.interface(model=User)
class UserType:
    id: strawberry.auto
    name: strawberry.auto

@strawberry.experimental.pydantic.type(model=NormalUser)
class NormalUserType(UserType):  # note the base class
    friends: strawberry.auto

@strawberry.experimental.pydantic.type(model=AdminUser)
class AdminUserType(UserType):
    role: strawberry.auto

Error Types

In addition to object types and input types, Strawberry allows you to create "error types". You can use these error types to have a typed representation of Pydantic errors in GraphQL. Let's see an example:

import pydantic
import strawberry

class User(BaseModel):
    id: int
    name: pydantic.constr(min_length=2)
    signup_ts: Optional[datetime] = None
    friends: List[int] = []

@strawberry.experimental.pydantic.error_type(model=User)
class UserError:
    id: strawberry.auto
    name: strawberry.auto
    friends: strawberry.auto

type UserError {
  id: [String!]
  name: [String!]
  friends: [[String!]]
}

where each field will hold a list of error messages

Extending types

You can use the usual Strawberry syntax to add additional new fields to the GraphQL type that aren't defined in the pydantic model

import strawberry
import pydantic

from .models import User

class User(BaseModel):
    id: int
    name: str

@strawberry.experimental.pydantic.type(model=User)
class User:
    id: strawberry.auto
    name: strawberry.auto
    age: int

type User {
    id: Int!
    name: String!
    age: Int!
}

Converting types

The generated types won't run any pydantic validation. This is to prevent confusion when extending types and also to be able to run validation exactly where it is needed.

To convert a Pydantic instance to a Strawberry instance you can use from_pydantic on the Strawberry type:

import strawberry
from typing import List, Optional
from pydantic import BaseModel


class User(BaseModel):
    id: int
    name: str


@strawberry.experimental.pydantic.type(model=User)
class UserType:
    id: strawberry.auto
    name: strawberry.auto
 
instance = User(id='123', name='Jake')

data = UserType.from_pydantic(instance)

If your Strawberry type includes additional fields that aren't defined in the pydantic model, you will need to use the extra parameter of from_pydantic to specify the values to assign to them.

import strawberry
from typing import List, Optional
from pydantic import BaseModel


class User(BaseModel):
    id: int
    name: str


@strawberry.experimental.pydantic.type(model=User)
class UserType:
    id: strawberry.auto
    name: strawberry.auto
    age: int

instance = User(id='123', name='Jake')

data = UserType.from_pydantic(instance, extra={'age': 10})

The data dictionary structure follows the structure of your data -- if you have a list of User, you should send an extra that is the list of User with the missing data (in this case, age).

You don't need to send all fields; data from the model is used first and then the extra parameter is used to fill in any additional missing data.

To convert a Strawberry instance to a pydantic instance and trigger validation, you can use to_pydantic on the Strawberry instance:

import strawberry
from typing import List, Optional
from pydantic import BaseModel


class User(BaseModel):
    id: int
    name: str


@strawberry.experimental.pydantic.input(model=User)
class UserInput:
    id: strawberry.auto
    name: strawberry.auto
 
input_data = UserInput(id='abc', name='Jake')

# this will run pydantic's validation
instance = input_data.to_pydantic()