Azure OpenAI api-version required or unsupported

"""Central AOAI client factory. Import get_client() everywhere."""
import os
import logging
from openai import AzureOpenAI
from azure.identity import DefaultAzureCredential, get_bearer_token_provider

log = logging.getLogger(__name__)

# The current recommended GA version — reviewed quarterly
DEFAULT_API_VERSION = "2024-10-21"

def get_client() -> AzureOpenAI:
    api_version = os.getenv("AZURE_OPENAI_API_VERSION", DEFAULT_API_VERSION)
    log.info("Instantiating AOAI client with api_version=%s", api_version)

    if os.getenv("AZURE_OPENAI_API_KEY"):
        return AzureOpenAI(
            api_key=os.environ["AZURE_OPENAI_API_KEY"],
            azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
            api_version=api_version,
        )
    # Entra ID
    credential = DefaultAzureCredential()
    provider = get_bearer_token_provider(credential, "https://cognitiveservices.azure.com/.default")
    return AzureOpenAI(
        azure_ad_token_provider=provider,
        azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
        api_version=api_version,
    )

# Usage everywhere in your codebase
client = get_client()

"""Probe AOAI at startup to verify feature support in the pinned api-version."""
import logging
from openai import AzureOpenAI, BadRequestError

log = logging.getLogger(__name__)

def probe_json_mode(client: AzureOpenAI, deployment: str) -> bool:
    try:
        client.chat.completions.create(
            model=deployment,
            messages=[{"role": "user", "content": "Return {\"ok\": true} as JSON."}],
            response_format={"type": "json_object"},
            max_tokens=20,
        )
        return True
    except BadRequestError as e:
        log.error("JSON mode probe failed: %s", e)
        return False

def probe_tool_choice(client: AzureOpenAI, deployment: str) -> bool:
    tools = [{"type": "function", "function": {
        "name": "noop", "description": "no-op",
        "parameters": {"type": "object", "properties": {}}}}]
    try:
        client.chat.completions.create(
            model=deployment,
            messages=[{"role": "user", "content": "call noop"}],
            tools=tools,
            tool_choice={"type": "function", "function": {"name": "noop"}},
            max_tokens=20,
        )
        return True
    except BadRequestError as e:
        log.error("tool_choice probe failed: %s", e)
        return False

def startup_probes(client, deployment, required_features):
    checks = {
        "json_mode": probe_json_mode,
        "tool_choice": probe_tool_choice,
    }
    for feature in required_features:
        ok = checks[feature](client, deployment)
        if not ok:
            raise RuntimeError(
                f"Feature '{feature}' not supported by pinned api-version. "
                f"Upgrade AZURE_OPENAI_API_VERSION."
            )
        log.info("Feature check %s: OK", feature)

# In app startup
# startup_probes(client, deployment="gpt4o-prod",
#                required_features=["json_mode", "tool_choice"])

# Quarterly Azure OpenAI api-version review

# 1) Current pinned version and where it is set
grep -r AZURE_OPENAI_API_VERSION infra/ config/ .env.*
grep -r api_version=\"20 src/    # find hardcoded versions in code

# 2) Check current status of the pinned version
# Azure OpenAI Reference docs -> API lifecycle table:
#   https://learn.microsoft.com/en-us/azure/ai-services/openai/api-version-deprecation

# 3) Is it approaching retirement (< 90 days)? If yes -> plan bump.
# 4) Are new features on newer versions that we would benefit from?
# 5) Any feature deprecations in newer versions that would break us?

# 6) Test bump in staging
export AZURE_OPENAI_API_VERSION=2024-11-01
pytest tests/aoai/  # covers all features you rely on
python startup_probe.py

# 7) Deploy to staging, run integration tests, monitor for 24h
# 8) Deploy to prod
# 9) Update the pinned version in docs and infra
# 10) Schedule next review 90 days out