Moderation Endpoint Schema Mismatch for illicit and illicit_violent fields

Confirm this is an issue with the Python library and not an underlying OpenAI API

[X] This is an issue with the Python library

Describe the bug

Description: The results returned by the moderation endpoint do not align with the expected schema.

Details:

In the Moderation.categories field (of type Categories), all fields are annotated as bool. However, when the moderation endpoint is called, the fields illicit and illicit_violent return None values instead of True or False.
The same issue occurs with the category_scores field (of type CategoryScores), where all fields are expected to be float. Yet, illicit and illicit_violent are also returned as None.

Expected Behavior:

If the None values for illicit and illicit_violent are expected behavior, these fields should be annotated as optional in the schema.
If this is not expected behavior, the API should be corrected to ensure that these fields return appropriate boolean or float values.

Additional Notes: It is surprising that Pydantic does not throw an error for these mismatches and allows None values to be returned. I could not manually create a Categories object with any None value in it.

To Reproduce

Run the moderation endpoint and check response.results[0] categories and category_scores -> illicit field.

Result I'm getting:

response.results: [
    Moderation(
        categories=Categories(
            harassment=False,
            harassment_threatening=False,
            hate=False,
            hate_threatening=False,
            illicit=None,
            illicit_violent=None,
            self_harm=False,
            self_harm_instructions=False,
            self_harm_intent=False,
            sexual=False,
            sexual_minors=False,
            violence=False,
            violence_graphic=False,
            self-harm=False,
            sexual/minors=False,
            hate/threatening=False,
            violence/graphic=False,
            self-harm/intent=False,
            self-harm/instructions=False,
            harassment/threatening=False,
        ),
        category_applied_input_types=None,
        category_scores=CategoryScores(
            harassment=0.000255020015174523,
            harassment_threatening=1.3588138244813308e-05,
            hate=2.8068381652701646e-05,
            hate_threatening=1.0663524108167621e-06,
            illicit=None,
            illicit_violent=None,
            self_harm=9.841909195529297e-05,
            self_harm_instructions=7.693658517382573e-06,
            self_harm_intent=7.031533459667116e-05,
            sexual=0.013590452261269093,
            sexual_minors=0.0031673426274210215,
            violence=0.00022930897830519825,
            violence_graphic=4.927426198264584e-05,
            self-harm=9.841909195529297e-05,
            sexual/minors=0.0031673426274210215,
            hate/threatening=1.0663524108167621e-06,
            violence/graphic=4.927426198264584e-05,
            self-harm/intent=7.031533459667116e-05,
            self-harm/instructions=7.693658517382573e-06,
            harassment/threatening=1.3588138244813308e-05,
        ),
        flagged=False,
    ),
]

Code snippets

import openai

client = openai.OpenAI()
response = client.moderations.create(input="text")
print(response.results[0])

OS

macOS

Python version

Python 3.12.4

Library version

openai 1.51.2

openai / openai-python