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-0.0.1.tar.gz (9.4 kB view details)

Uploaded Source

Built Distribution

File details

Details for the file graphql_pydantic_transformer-0.0.1.tar.gz.

File metadata

File hashes

Hashes for graphql_pydantic_transformer-0.0.1.tar.gz
Algorithm Hash digest
SHA256 6ea6567cecdbf05b53d88a4f4701ef4e081a4265bcdacce93b2038a3a5c88355
MD5 8b3e7a79d93dd37433e248a7d3031d4a
BLAKE2b-256 2b54f5f4343849825ea651dd276d5c796f79db36ace1a7ce758b1662def0adc5

See more details on using hashes here.

File details

Details for the file graphql_pydantic_transformer-0.0.1-py3-none-any.whl.

File metadata

File hashes

Hashes for graphql_pydantic_transformer-0.0.1-py3-none-any.whl
Algorithm Hash digest
SHA256 bcd0d22b001505526447cf1a98ac3b70e631136222332300ee16d1284d6d4d7d
MD5 f81d5adff2a22d722d816a5733aaa40a
BLAKE2b-256 51c081e7a131724cf6fa37bafdd9ccb0aec4be14a52892833f43809b480a4913

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