Graphene Pydantic Integration

Warnings

• breaking Version 0.3.0 dropped support for Python 3.6 and Pydantic versions older than 1.7. Ensure your environment meets these minimum requirements.
  Fix: Upgrade Python to 3.7+ and Pydantic to 1.7+ before upgrading graphene-pydantic to 0.3.0 or later.
• breaking Version 0.1.0 removed support for Pydantic 0.x. If migrating from very old Pydantic versions, significant changes may be required.
  Fix: Upgrade Pydantic to a 1.x version first, addressing any Pydantic-specific breaking changes, then upgrade graphene-pydantic.
• gotcha Due to a GraphQL limitation, Pydantic fields that hold mappings (e.g., dictionaries) cannot be directly exported to Graphene types.
  Fix: Represent dictionary fields as `JSONString` or define custom scalar types/Graphene `ObjectType`s for complex mapping structures, and provide custom resolvers.
• gotcha GraphQL Input Object Types do not support unions as fields. Attempting to use a Pydantic `Union` type in an `PydanticInputObjectType` will lead to errors.
  Fix: Avoid `Union` types in `PydanticInputObjectType` fields. Consider using separate input types for each union member or redesigning the input structure.
• gotcha When using `Union` types in `PydanticObjectType`, you must explicitly implement the `is_type_of` class method in your Graphene models. For unions between subclasses, the subclass must be listed first in the type annotation to ensure correct resolution.
  Fix: Implement `is_type_of` in Graphene models representing union members. For `Union[Subclass, Baseclass]`, define as `Union[Subclass, Baseclass]`.
• gotcha The library supports Pydantic versions `~1.7` through `~2.x`. However, Pydantic itself introduced significant breaking changes between V1 and V2. While `graphene-pydantic` aims to be compatible, migrating your underlying Pydantic models from V1 to V2 may still require substantial refactoring.
  Fix: Refer to Pydantic's official migration guide for V1 to V2 changes. Use Pydantic's `bump-pydantic` tool for automated code transformation where possible. Thoroughly test your Graphene schema after Pydantic model updates.

Install

• pip install "graphene-pydantic" Install stable version

Imports

• PydanticObjectType

  from graphene_pydantic import PydanticObjectType

• PydanticInputObjectType

  from graphene_pydantic import PydanticInputObjectType

Quickstart

This quickstart demonstrates how to define a Pydantic model and automatically convert it into a Graphene `ObjectType` using `PydanticObjectType`. It then sets up a basic GraphQL schema and executes a sample query.

import uuid
import pydantic
import graphene
from graphene_pydantic import PydanticObjectType

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

class Person(PydanticObjectType):
    class Meta:
        model = PersonModel
        exclude_fields = ("id",)

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

    @staticmethod
    def resolve_people(parent, info):
        # In a real application, you would fetch data from a database
        return [
            PersonModel(id=uuid.uuid4(), first_name="Alice", last_name="Smith"),
            PersonModel(id=uuid.uuid4(), first_name="Bob", last_name="Johnson")
        ]

schema = graphene.Schema(query=Query)

query = """
query {
  people {
    firstName,
    lastName
  }
}
"""

result = schema.execute(query)
print(result.data['people'])
# Expected output: [{'firstName': 'Alice', 'lastName': 'Smith'}, {'firstName': 'Bob', 'lastName': 'Johnson'}]