Skip to main content

Convert pydantic schema to pydantic datamodel and build request from it

Project description

GraphQL to Pydantic Transformer & Pydantic to Query Builder

Overview

The GraphQL to Pydantic Transformer is a Python package designed to simplify the process of transforming GraphQL schemas in JSON format into Pydantic models. This tool is particularly useful for developers working with GraphQL APIs who want to generate Pydantic models from GraphQL types for efficient data validation and serialization/deserialization.

Features

  • Converts GraphQL schemas in JSON format into Pydantic models.
  • Build query or mutation from pydantic dataclass

Installation

You can install the GraphQL to Pydantic Transformer package via pip:

pip install graphql-pydantic-transformer

Usage

Cli tool to transform GraphQL JSON to Pydantic

graphql-pydantic-converter [-h] [-i INPUT_FILE] [-o OUTPUT_FILE] [--url URL] [--headers HEADERS [HEADERS ...] ]

options:
  -h, --help            show this help message and exit
  -i INPUT_FILE, --input-file INPUT_FILE 
  -o OUTPUT_FILE, --output-file OUTPUT_FILE      
  --url URL 
  --headers HEADERS [HEADERS ...] # --headers "HeaderName: HeaderValue" "HeaderName: HeaderValue"

Output from cli tool

import typing

from pydantic import Field
from graphql_pydantic_converter.graphql_types import Input
from graphql_pydantic_converter.graphql_types import Mutation
from graphql_pydantic_converter.graphql_types import Payload

Boolean: typing.TypeAlias = bool
DateTime: typing.TypeAlias = typing.Any
Float: typing.TypeAlias = float
ID: typing.TypeAlias = str
Int: typing.TypeAlias = int
JSON: typing.TypeAlias = typing.Any
String: typing.TypeAlias = str

class CreateScheduleInput(Input):
    name: String
    workflow_name: String = Field(alias='workflowName')
    workflow_version: String = Field(alias='workflowVersion')
    cron_string: String = Field(alias='cronString')
    enabled: typing.Optional[Boolean]
    parallel_runs: typing.Optional[Boolean] = Field(alias='parallelRuns')
    workflow_context: typing.Optional[String] = Field(alias='workflowContext')
    from_date: typing.Optional[DateTime] = Field(alias='fromDate')
    to_date: typing.Optional[DateTime] = Field(alias='toDate')

class Schedule(Payload):
    name: typing.Optional[Boolean] = Field(response='String', default=True)
    enabled: typing.Optional[Boolean] = Field(response='Boolean', default=True)
    parallel_runs: typing.Optional[Boolean] = Field(response='Boolean', alias='parallelRuns', default=True)
    workflow_name: typing.Optional[Boolean] = Field(response='String', alias='workflowName', default=True)
    workflow_version: typing.Optional[Boolean] = Field(response='String', alias='workflowVersion', default=True)
    cron_string: typing.Optional[Boolean] = Field(response='String', alias='cronString', default=True)
    workflow_context: typing.Optional[Boolean] = Field(response='String', alias='workflowContext', default=True)
    from_date: typing.Optional[Boolean] = Field(response='DateTime', alias='fromDate', default=True)
    to_date: typing.Optional[Boolean] = Field(response='DateTime', alias='toDate', default=True)
    status: typing.Optional[Boolean] = Field(response='Status', default=True)


class CreateScheduleMutation(Mutation):
    _name: str = Field('createSchedule', const=True)
    input: CreateScheduleInput
    payload: Schedule

CreateScheduleInput.update_forward_refs()
CreateScheduleMutation.update_forward_refs()
Schedule.update_forward_refs()

Query & Mutation builder

from schedule_api import Schedule, CreateScheduleMutation, CreateScheduleInput

SCHEDULE: Schedule = Schedule(
    name=True,
    enabled=True,
    workflowName=True,
    workflowVersion=True,
    cronString=True,
    fromDate=False,
    toDate=False,
    status=False
)

mutation = CreateScheduleMutation(
    payload=SCHEDULE,
    input=CreateScheduleInput(
        name='name',
        workflowName='workflowName',
        workflowVersion='workflowVersion',
        cronString='* * * * *',
        enabled=True,
        parallelRuns=False,
    )
)

mutation.render()

Created query

mutation {
  createSchedule(
    input: {
      name: "name"
      workflowName: "workflowName"
      workflowVersion: "workflowVersion"
      cronString: "* * * * *"
      enabled: true
      parallelRuns: false
    }
  ) {
    name
    enabled
    parallelRuns
    workflowName
    workflowVersion
    cronString
    workflowContext
  }
}

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

graphql_pydantic_transformer-0.0.2.tar.gz (9.4 kB view details)

Uploaded Source

Built Distribution

File details

Details for the file graphql_pydantic_transformer-0.0.2.tar.gz.

File metadata

File hashes

Hashes for graphql_pydantic_transformer-0.0.2.tar.gz
Algorithm Hash digest
SHA256 17c569eeea24cc90e201fb14ebdf7056e0a7d04415ac20d8dbba649eb44e4973
MD5 56cece35ae231a2b7a49ba1d95748f44
BLAKE2b-256 b2026e4b4b82c1f70532ff49624e343be33885ccb8d06125a7aaae0970281d63

See more details on using hashes here.

File details

Details for the file graphql_pydantic_transformer-0.0.2-py3-none-any.whl.

File metadata

File hashes

Hashes for graphql_pydantic_transformer-0.0.2-py3-none-any.whl
Algorithm Hash digest
SHA256 2e130314658f5e20dac0d154864504d032a33d457c54719e36c08c0703b92df1
MD5 9c4320b420c636f6d4f79f7dc72366fd
BLAKE2b-256 106db455679152d22d23a2e73c36a89a1e3eeef9f9b6037e45e363c9e9de3c70

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