Python Consumer¶

Preface¶

This guide will explain how to fetch data from a GraphQL endpoint and map the response to a Pydantic model. We will cover:

Defining a Pydantic model in Define the Pydantic Model
Fetching the data from GraphQL API in Fetch Data from GraphQL API
Mapping the response to the Pydantic model in Map Response Pydantic Model

Alternative Options

You may want to investigate the following libraries depending on your specific needs:

- gql if you require support for subscriptions - sgqlc for typed code generation - ariadne-codegen for pydantic compatible code generation

Dependencies¶

This guide will utilize the following dependencies:

requests to send HTTP request to the GraphQL endpoint
pydantic for validating and parsing the fetched data

Define the Pydantic Model¶

More about Pydantic

Pydantic is a data validation and parsing library in Python. It provides a efficient way to validate and parse data into Python objects. We can use pydantic models to define the structure and type of data we intend to fetch from the endpoint. It automatically validates the input data based on the defined type annotations. If the data does not match the expected types, Pydantic raises errors ensuring the integrity of the data.

To create a Pydantic model, we define a class with Pydantic's BaseModel class as the base class. We can define a Person class with the attributes we intend to fetch from the endpoint.

Define Pydantic Model

from pydantic import BaseModel, ConfigDict

class Person(BaseModel):
    model_config = ConfigDict(strict=True)
    id: int
    first_name: str
    last_name: str

model_config is a way to configure model-level settings using a structured approach. ConfigDict allows for specifying configuration options in a more readable and organized manner.

strict=True This setting ensures strict type validation, meaning that types must match exactly as defined in the model.

Info

Refer to the Graphql Schemas and Types to learn more about the GraphQL schema and API structure

Fetch Data From GraphQL API¶

To fetch data from a GraphQL API, we use the requests library to send a POST request with a GraphQL query. We specify the GraphQL query as a string.

Fetch Data

import requests

url = 'http://127.0.0.1:8000/graphql'
graphql_query = """
    query {
        person {
            id,
            firstName,
            lastName,
        }
    }
"""

response = requests.post(url, json={'query': graphql_query})

Map Response to Pydantic Model¶

We can now map the json response we got from the endpoint to a Pydantic model by using Pydantics model_validate_json() method

Define Pydantic Model

response_data = response.json()

# Extract the person data from the response
person_data = response_data['data']['person']

# Map the GraphQL response fields to the Pydantic model fields
person = Person.model_validate_json(person_data)

If the Python graphQL service is running, person should output:

id=1 first_name='foo' last_name='bar'