Mark Backman b64ed3f9e2 Narrow settings.model at service boundaries, not via truthiness ...

Two services were reading `_settings.model` (typed `str | _NotGiven |
None` because NOT_GIVEN is the default) and coercing it with `or ""`
or similar. `_NotGiven.__bool__` returns False, so the runtime
behavior happened to work, but the type was a lie — pyright saw
`str | _NotGiven` flowing into APIs that required `str` or `str | None`.

- `AIService._sync_model_name_to_metrics`: use `isinstance(model, str)`
  narrowing with an empty-string fallback. Equivalent runtime behavior,
  honest type, no truthiness dependency on a sentinel.
- `SarvamLLMService.__init__`: validate the model is a real string
  before handing it to `_validate_model(str)`. A non-string model at
  this point is a configuration bug; raise `ValueError` so the error
  is clear and survives `python -O` (unlike an assert).

2026-04-22 11:52:20 -04:00

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