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graphql-pydantic-converter
Advanced tools
Convert pydantic schema to pydantic datamodel and build request from it
The GraphQL to Pydantic Converter 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.
You can install the GraphQL to Pydantic Transformer package via pip:
pip install graphql-pydantic-converter
# or
poetry add git+https://github.com/FRINXio/frinx-services-python-api.git@main#subdirectory=utils/graphql-pydantic-converter
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"
import typing
from pydantic import Field
from pydantic import PrivateAttr
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(default=None, alias='workflowName')
workflow_version: String = Field(default=None, alias='workflowVersion')
cron_string: String = Field(default=None, alias='cronString')
enabled: typing.Optional[Boolean] = Field(default=None)
parallel_runs: typing.Optional[Boolean] = Field(default=None, alias='parallelRuns')
workflow_context: typing.Optional[String] = Field(default=None, alias='workflowContext')
from_date: typing.Optional[DateTime] = Field(default=None, alias='fromDate')
to_date: typing.Optional[DateTime] = Field(default=None, alias='toDate')
class Schedule(Payload):
name: typing.Optional[bool] = Field(default=False)
enabled: typing.Optional[bool] = Field(default=False)
parallel_runs: typing.Optional[bool] = Field(alias='parallelRuns', default=False)
workflow_name: typing.Optional[bool] = Field(alias='workflowName', default=False)
workflow_version: typing.Optional[bool] = Field(alias='workflowVersion', default=False)
cron_string: typing.Optional[bool] = Field(alias='cronString', default=False)
workflow_context: typing.Optional[bool] = Field(alias='workflowContext', default=False)
from_date: typing.Optional[bool] = Field(alias='fromDate', default=False)
to_date: typing.Optional[bool] = Field(alias='toDate', default=False)
status: typing.Optional[bool] = Field(default=False)
class CreateScheduleMutation(Mutation):
_name: str = PrivateAttr('createSchedule')
input: CreateScheduleInput
payload: Schedule
CreateScheduleInput.model_rebuild()
CreateScheduleMutation.model_rebuild()
Schedule.model_rebuild()
from schedule_api import Schedule, CreateScheduleMutation, CreateScheduleInput
SCHEDULE: Schedule = Schedule(
name=True,
enabled=True,
workflow_name=True,
workflow_version=True,
cron_string=True
)
mutation = CreateScheduleMutation(
payload=SCHEDULE,
input=CreateScheduleInput(
name='name',
workflow_name='workflowName',
workflow_version='workflowVersion',
cron_string='* * * * *',
enabled=True,
parallel_runs=False,
)
)
Created query with inlined variables as string
mutation.render(form='inline')
mutation {
createSchedule(
input: {
name: "name"
workflowName: "workflowName"
workflowVersion: "workflowVersion"
cronString: "* * * * *"
enabled: true
parallelRuns: false
}
) {
name
enabled
workflowName
workflowVersion
cronString
}
}
Created query with extracted variables
mutation, variables = mutation.render(form='extracted)
# mutation as a string
mutation ($input: CreateScheduleInput!) {
createSchedule(input: $input) {
name
enabled
workflowName
workflowVersion
cronString
}
}
# variables as a dict[str, Any]
{
"input": {
"name": "name",
"workflowName": "workflowName",
"workflowVersion": "workflowVersion",
"cronString": "* * * * *",
"enabled": true,
"parallelRuns": false
}
}
Example of generated model.py
import typing
from pydantic import BaseModel, Field
from graphql_pydantic_converter.graphql_types import ENUM
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 Status(ENUM):
UNKNOWN = 'UNKNOWN'
COMPLETED = 'COMPLETED'
FAILED = 'FAILED'
PAUSED = 'PAUSED'
RUNNING = 'RUNNING'
TERMINATED = 'TERMINATED'
TIMED_OUT = 'TIMED_OUT'
class SchedulePayload(BaseModel):
name: typing.Optional[typing.Optional[String]] = Field(default=None)
enabled: typing.Optional[typing.Optional[Boolean]] = Field(default=None)
parallel_runs: typing.Optional[typing.Optional[Boolean]] = Field(default=None, alias='parallelRuns')
workflow_name: typing.Optional[typing.Optional[String]] = Field(default=None, alias='workflowName')
workflow_version: typing.Optional[typing.Optional[String]] = Field(default=None, alias='workflowVersion')
cron_string: typing.Optional[typing.Optional[String]] = Field(default=None, alias='cronString')
workflow_context: typing.Optional[typing.Optional[String]] = Field(default=None, alias='workflowContext')
from_date: typing.Optional[typing.Optional[DateTime]] = Field(default=None, alias='fromDate')
to_date: typing.Optional[typing.Optional[DateTime]] = Field(default=None, alias='toDate')
status: typing.Optional[typing.Optional[Status]] = Field(default=None)
class CreateScheduleData(BaseModel):
create_schedule: SchedulePayload = Field(default=None, alias='createSchedule')
class CreateScheduleResponse(BaseModel):
data: typing.Optional[CreateScheduleData] = Field(default=None)
errors: typing.Optional[typing.Any] = Field(default=None)
from model import CreateScheduleResponse
# send previously created request to backend service
payload = {'query': mutation.render()}
resp = requests.post(SCHELLAR_URL, json=payload)
response = resp.json()
# Example of response
# {
# 'data': {
# 'createSchedule': {
# 'name': 'name',
# 'enabled': True,
# 'workflowName': 'workflowName',
# 'workflowVersion': 'workflowVersion',
# 'cronString': '* * * * *'
# }
# }
# }
schedule = CreateScheduleResponse(**response)
if schedule.errors is None:
print(schedule.data.create_schedule.workflow_name)
else:
print(schedule.errors)
FAQs
Convert pydantic schema to pydantic datamodel and build request from it
We found that graphql-pydantic-converter demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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