fix(openai): default Azure LLM model to deployment

[nightcityblade]

Fixes #6209

Summary

• stop LLM.with_azure() from exposing the unrelated gpt-4o default when Azure callers only set azure_deployment
• use the Azure deployment name as the fallback model identifier so telemetry and llm.model reflect the configured deployment
• add a regression test covering the Azure fallback behavior

Testing

• uv run ruff check livekit-plugins/livekit-plugins-openai/livekit/plugins/openai/llm.py tests/test_config.py
• uv run pytest tests/test_config.py

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[devin-ai-integration]

✅ Devin Review: No Issues Found

Devin Review analyzed this PR and found no bugs or issues to report.

[chenghao-mou]

We can simply use unknown as the last resort, as the model parameter is only used for metric/label.

[nightcityblade]

Addressed the review note in 4c0ede6. LLM.with_azure() now falls back to "unknown" when neither an explicit model nor an Azure deployment is provided, so we keep the metrics label neutral instead of defaulting to a specific model family. I also added a focused regression test for the no-model/no-deployment case.

Validation:

• uv run pytest tests/test_config.py
• uv run ruff check livekit-plugins/livekit-plugins-openai/livekit/plugins/openai/llm.py tests/test_config.py

[nightcityblade]

I pushed a follow-up commit to align with the review suggestion: Azure now keeps as the fallback model label unless the caller explicitly provides , and I updated the config expectation to match.\n\nI also rechecked the simple constructor path locally against the new behavior.

[nightcityblade]

I pushed a follow-up commit to align with the review suggestion: Azure now keeps "unknown" as the fallback model label unless the caller explicitly provides model, and I updated the config expectation to match.

I also rechecked the simple LLM.with_azure(...) constructor path locally against the new behavior.

[nightcityblade]

Followed up in 274decd to narrow the fallback exactly the way suggested.

Behavior now:

• if model is provided, keep it
• if azure_deployment is provided and model is omitted, keep the neutral "unknown" metric label
• if neither model nor azure_deployment is provided, fall back to the previous "gpt-4o" value so we do not change the invalid no-deployment request-routing path

Validation rerun:

• uv run pytest tests/test_config.py -q
• uv run ruff check livekit-plugins/livekit-plugins-openai/livekit/plugins/openai/llm.py tests/test_config.py

[devin-ai-integration]

Devin Review found 1 new potential issue.

[devin-ai-integration]

🚩 Behavioral change for existing Azure users who relied on model='gpt-4o' with deployments

Previously, all LLM.with_azure() callers got model='gpt-4o' regardless of whether they specified azure_deployment. Now, callers who pass azure_deployment without an explicit model will see llm.model return "unknown" instead of "gpt-4o". This affects any downstream code that reads llm.model for metrics labels, logging, or conditional logic. The code comment at livekit-plugins/livekit-plugins-openai/livekit/plugins/openai/llm.py:241-250 states this is intentional ("Keep Azure metric labels neutral"), but it is a silent semantic change for existing integrations that may have dashboards or alerts keyed on model name.

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