GraphQL

I have a custom input object type

class Filters(graphene.InputObjectType):                                        
    name = graphene.String()
    type = graphene.List(graphene.String)

and the query definition

class Query(graphene.ObjectType):                                               
    search = graphene.Field(
        SearchResult, query=graphene.String(require

Add a note on the TestClient section about the behavior difference between those two ways, i.e. lifespan events.

Considering the follow application:

from starlette.applications import Starlette
from starlette.requests import Request
from starlette.responses import Response
from starlette.routing import Route
from starlette.testclient import TestClient


async def startup() 

Hey! When I was reading the docs and I saw api.background.task, the first thing that popped into my head was "but you can just use asyncio.create_task or loop,.run_in_executor for that without inventing something new!". But then I noticed that it also passes the context vars to the synchronous backround stuff, which is nice. Wonder why that's not the case in the stdlib, cause it would be nic

We only have documentation for schema directives, we should document query directives, here's an example of how they work: https://github.com/strawberry-graphql/strawberry/blob/main/tests/schema/test_directives.py

This might be helpful - too many redirects may be considered an error. E.g. the API schema might need to provide endpoints that won't involve any redirection at all, for example, if the app redirects "/api/users" to "/api/users/", then the schema should have endpoints with trailing slashes to avoid these redirects for clients at all.

Implementation notes

• Update models.Case.call signatu

Make a concise print function for the QueryPlanningAnalysis class https://github.com/kensho-technologies/graphql-compiler/blob/v2.0.0.dev25/graphql_compiler/cost_estimation/analysis.py#L381

In most cases, the printout of the analysis passes is enough to explain why a particular query plan was chosen.

Loving this project.

I feel like this project would benefit from a short quick start tutorial that demonstrate common methods such as:

Create item

    mutation = Operation(schema.Mutation)  # note 'schema.'
    mutation.create_item(input=dict(id="23432")).__fields__()

List items with filter

    op = Operation(schema.Query)  # note 'schema.'
    op.list_it