query

reading data from apis is the most common operation we do, that's why ql makes it easy to query data from your graphql endpoint and provide variety of query methods.

ql.query

takes python ql query structure and returns a valid graphql query string.

def query(
    *query_models: _QueryModelType,
    fragments: Optional[_QueryFragmentType] = None,
    include_typename: bool = True,
) -> str:

Name	Type	Description
query_models	*_QueryModelType	python ql structured query
fragments	Optional[_QueryFragmentType]	dict mapping between ql.fragment to the python ql structured query
include_typename	bool	if include __typename field when querying sub types

example.py

import ql
from pydantic import BaseModel

@ql.model
class Point(BaseModel):
  x: int
  y: int

query_str = ql.query(
  (Point, (
    ql._(Point).x,
    ql._(Point).y
  ))
)

ql.query_response

serializes the ql query structure, send it via http and returns the response as dict.

def query_response(
    *query_models: _QueryModelType,
    fragments: Optional[_QueryFragmentType] = {},
    include_typename: bool = True,
) -> QueryResponseDict:

http request function must be set to make this function work, click here to view.

ql.query_response_scalar

serializes the ql query structure to a valid graphql query, send it via http, takes the response and returns a scalared dict with the defined models, this function will also raise graphql errors if the query responsed with errors field

def query_response_scalar(
    *query_models: _QueryModelType, fragments: Optional[_QueryFragmentType] = None
) -> dict[str, BaseModel | list[BaseModel]]: