fastgraphql 0.0.3

FastGraphQL is intended to help developer create code driven GraphQL APIs

FastGraphQL

FastGraphQL is intended to help developer create code driven GraphQL APIs.

Disclaimer

This is still a work in progress

Motivation

So far most of the projects that uses GraphQL need to duplicate many definitions to be able to have a consistent GraphQL API schema alongside well-defined models that governs the development and the application.

FastGraphQL tries to shortcut the path between python models and GraphQL schema using Pydantic models. This ensures not only a single source of truth when comes to type, inputs, query and mutation definition reflected in classes and methods, but also the ability to use Pydantic to validate models.

Installation

pip install fastgraphql

Usage

GraphQL Types and Inputs

Using annotation driven definitions and Pydantic, defining GraphQL types and inputs can be done by simple annotating Pydantic models with FastGraphQL.graphql_type() of FastGraphQL.graphql_input()

from datetime import datetime
from typing import Optional
from pydantic import BaseModel
from fastgraphql import FastGraphQL

fast_graphql = FastGraphQL()

@fast_graphql.graphql_type()
class Model(BaseModel):
    t_int: int
    t_opt_int: Optional[int]
    t_str: str
    t_opt_str: Optional[str]
    t_float: float
    t_opt_float: Optional[float]
    t_datatime: datetime
    t_opt_datatime: Optional[datetime]
    t_boolean: bool
    t_opt_boolean: Optional[bool]

@fast_graphql.graphql_type()
class Input(BaseModel):
    t_int: int
    
print(fast_graphql.render())

The above code example generates a schema as follows:

scalar DateTime

type Model {
    t_int: Int!
    t_opt_int: Int
    t_str: String!
    t_opt_str: String
    t_float: Float!
    t_opt_float: Float
    t_datatime: DateTime!
    t_opt_datatime: DateTime
    t_boolean: Boolean!
    t_opt_boolean: Boolean
}

type Input {
    t_int: Int!
}

Query and Mutation

Following the same approach with annotation driven defitions, query and mutations can easily be defined using FastGraphQL.graphql_query and FastGraphQL.mutation.

Note that all function arguments annotated with FastGraphQL.graphql_query_field are considered to be input arguments for the GraphQL API and simples types and Pydantic models can be used and arguments and also as return type and they don't need to be explicitly annotated.

from fastgraphql import FastGraphQL
from pydantic import BaseModel
fast_graphql = FastGraphQL()

class Model(BaseModel):
    param: str

@fast_graphql.graphql_query()
def my_first_query(
        model: Model = fast_graphql.graphql_query_field(),
        param: str = fast_graphql.graphql_query_field()
) -> str:
    ...

print(fast_graphql.render())

The above code example generates a schema as follows:

input Model {
    param: String!
}
type Query {
    my_first_query(model: Model!, param: String!): String!
}

Dependecy Injection

Query and Mutation can have dependencies injected using FastGraphQL.depende(...) as showed bellow:`

from fastgraphql import FastGraphQL
from pydantic import BaseModel
fast_graphql = FastGraphQL()

class Model(BaseModel):
    param: str

def create_dependency() -> str:
    return ""
    
@fast_graphql.graphql_query()
def my_first_query(
        model: Model = fast_graphql.graphql_query_field(),
        dependecy: str = fast_graphql.depends(create_dependency)
) -> str:
    ...

In this example the parameter dependecy will be injected once the query is called.

Integrations

Ariadne

...

FastAPI

...

Acknowledgment

Thanks FastAPI for inpirations