Skip to main content

Graphene Pydantic integration

Project description

Graphene Logo graphene-pydantic Build status PyPI version Coverage Status

A Pydantic integration for Graphene.

Installation

pip install "graphene-pydantic"

Examples

Here is a simple Pydantic model:

import uuid
import pydantic

class PersonModel(pydantic.BaseModel):
    id: uuid.UUID
    first_name: str
    last_name: str

To create a GraphQL schema for it you simply have to write the following:

import graphene
from graphene_pydantic import PydanticObjectType

class Person(PydanticObjectType):
    class Meta:
        model = PersonModel
        # exclude specified fields
        exclude_fields = ("id",)

class Query(graphene.ObjectType):
    people = graphene.List(Person)

    @staticmethod
    def resolve_people(parent, info):
        # fetch actual PersonModels here
        return [PersonModel(id=uuid.uuid4(), first_name="Beth", last_name="Smith")]

schema = graphene.Schema(query=Query)

Then you can simply query the schema:

query = """
    query {
      people {
        firstName,
        lastName
      }
    }
"""
result = schema.execute(query)
print(result.data['people'][0])

Input Object Types

You can also create input object types from Pydantic models for mutations and queries:

from graphene_pydantic import PydanticInputObjectType

class PersonInput(PydanticInputObjectType):
    class Meta:
        model = PersonModel
        # exclude specified fields
        exclude_fields = ("id",)

class CreatePerson(graphene.Mutation):
    class Arguments:
        person = PersonInput()

    Output = Person

    @staticmethod
    def mutate(parent, info, person):
        personModel = PersonModel(id=uuid.uuid4(), first_name=person.first_name, last_name=person.last_name)
        # save PersonModel here
        return person

class Mutation(graphene.ObjectType):
    createPerson = CreatePerson.Field()

schema = graphene.Schema(mutation=Mutation)

Then execute with the input:

mutation = '''
mutation {
    createPerson(person: {
        firstName: "Jerry",
        lastName: "Smith"
    }) {
        firstName
    }
}
'''
result = schema.execute(mutation)
print(result.data['createPerson']['firstName'])

Custom resolve functions

Since PydanticObjectType inherits from graphene.ObjectType you can add custom resolve functions as explained here. For instance:

class Person(PydanticObjectType):
    class Meta:
        model = PersonModel
        # exclude specified fields
        exclude_fields = ("id",)

    full_name = graphene.String()

    def resolve_full_name(self, info, **kwargs):
        return self.first_name + ' ' + self.last_name

Forward declarations and circular references

graphene_pydantic supports forward declarations and circular references, but you will need to call the resolve_placeholders() method to ensure the types are fully updated before you execute a GraphQL query. For instance:

class NodeModel(BaseModel):
    id: int
    name: str
    labels: 'LabelsModel'

class LabelsModel(BaseModel):
    node: NodeModel
    labels: typing.List[str]

class Node(PydanticObjectType):
    class Meta:
        model = NodeModel

class Labels(PydanticObjectType):
    class Meta:
        model = LabelsModel


Node.resolve_placeholders()  # make the `labels` field work
Labels.resolve_placeholders()  # make the `node` field work

Full Examples

Please see the examples directory for more.

License

This project is under the Apache License.

Third Party Code

This project depends on third-party code which is subject to the licenses set forth in Third Party Licenses.

Contributing

Please see the Contributing Guide.

Caveats

Mappings

Note that even though Pydantic is perfectly happy with fields that hold mappings (e.g. dictionaries), because GraphQL's type system doesn't have them those fields can't be exported to Graphene types. For instance, this will fail with an error Don't know how to handle mappings in Graphene:

import typing
from graphene_pydantic import PydanticObjectType

class Pet:
    pass

class Person:
    name: str
    pets_by_name: typing.Dict[str, Pet]

class GraphQLPerson(PydanticObjectType):
    class Meta:
        model = Person

However, note that if you use exclude_fields or only_fields to exclude those values, there won't be a problem:

class GraphQLPerson(PydanticObjectType):
    class Meta:
        model = Person
        exclude_fields = ("pets_by_name",)

Union types

There are some caveats when using Unions. Let's take the following pydantic models as an example for this section:

class EmployeeModel(pydantic.BaseModel):
    name: str


class ManagerModel(EmployeeModel):
    title: str


class DepartmentModel(pydantic.BaseModel):
    employees: T.List[T.Union[ManagerModel, EmployeeModel]]
You have to implement the class method is_type_of in the graphene models

To get the Union between ManagerModel and EmployeeModel to successfully resolve in graphene, you need to implement is_type_of like this:

class Employee(PydanticObjectType):
    class Meta:
        model = EmployeeModel

    @classmethod
    def is_type_of(cls, root, info):
        return isinstance(root, (cls, EmployeeModel))


class Manager(PydanticObjectType):
    class Meta:
        model = ManagerModel

    @classmethod
    def is_type_of(cls, root, info):
        return isinstance(root, (cls, ManagerModel))


class Department(PydanticObjectType):
    class Meta:
        model = DepartmentModel

Otherwise GraphQL will throw an error similar to "[GraphQLError('Abstract type UnionOfManagerModelEmployeeModel must resolve to an Object type at runtime for field Department.employees ..."

For unions between subclasses, you need to put the subclass first in the type annotation

Looking at the employees field above, if you write the type annotation with Employee first, employees: T.List[T.Union[EmployeeModel, ManagerModel]], you will not be able to query manager-related fields (in this case title). In a query containing a spread like this:

...on Employee {
  name
}
...on Manager {
  name
  title
}

... the objects will always resolve to being an Employee. This can be avoided if you put the subclass first in the list of annotations: employees: T.List[T.Union[ManagerModel, EmployeeModel]].

Unions between subclasses don't work in Python 3.6

If a field on a model is a Union between a class and a subclass (as in our example), Python 3.6's typing will not preserve the Union and throws away the annotation for the subclass. See this issue for more details. The solution at present is to use Python 3.7.

Input Object Types don't support unions as fields

This is a GraphQL limitation. See this RFC for the progress on supporting input unions. If you see an error like '{union-type} may only contain Object types', you are most likely encountering this limitation.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

graphene_pydantic-0.6.1.tar.gz (13.4 kB view details)

Uploaded Source

Built Distribution

graphene_pydantic-0.6.1-py3-none-any.whl (14.5 kB view details)

Uploaded Python 3

File details

Details for the file graphene_pydantic-0.6.1.tar.gz.

File metadata

  • Download URL: graphene_pydantic-0.6.1.tar.gz
  • Upload date:
  • Size: 13.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.4.0 CPython/3.11.7 Linux/5.15.0-91-generic

File hashes

Hashes for graphene_pydantic-0.6.1.tar.gz
Algorithm Hash digest
SHA256 838e80d4390b67188b4b94498c33f82bdbf44e1ad4b7c3ba9b953b55680fa8f5
MD5 5bf4d96cfe9300a53413598304dcb281
BLAKE2b-256 240ecefedcec85f4b8bc2b6c90bde2beb0651b080bd04debeb43bf747ce14140

See more details on using hashes here.

File details

Details for the file graphene_pydantic-0.6.1-py3-none-any.whl.

File metadata

  • Download URL: graphene_pydantic-0.6.1-py3-none-any.whl
  • Upload date:
  • Size: 14.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.4.0 CPython/3.11.7 Linux/5.15.0-91-generic

File hashes

Hashes for graphene_pydantic-0.6.1-py3-none-any.whl
Algorithm Hash digest
SHA256 c1ffdb2003e5cd9d94e79904459075bcf25a397506216d7af7b757bf302d0a19
MD5 2a1ff3b108e973fe15f4da6e159eeca9
BLAKE2b-256 b8010b6f35b5550e6fc997ded55eeb57ce677d6d48887256eb372a0284392169

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page