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fix: Avoid model_limits KeyError (backport #4060) #4283

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Dec 3, 2025
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fix: Avoid model_limits KeyError (backport #4060) #4283

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8 changes: 2 additions & 6 deletions llama_stack/providers/remote/inference/watsonx/watsonx.py
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Original file line number Diff line number Diff line change
Expand Up @@ -283,8 +283,8 @@ async def list_models(self) -> list[Model] | None:
# ...
provider_resource_id = f"{self.__provider_id__}/{model_spec['model_id']}"
if "embedding" in functions:
embedding_dimension = model_spec["model_limits"]["embedding_dimension"]
context_length = model_spec["model_limits"]["max_sequence_length"]
embedding_dimension = model_spec.get("model_limits", {}).get("embedding_dimension", 0)
context_length = model_spec.get("model_limits", {}).get("max_sequence_length", 0)
embedding_metadata = {
"embedding_dimension": embedding_dimension,
"context_length": context_length,
Expand All @@ -306,10 +306,6 @@ async def list_models(self) -> list[Model] | None:
metadata={},
model_type=ModelType.llm,
)
# In theory, I guess it is possible that a model could be both an embedding model and a text chat model.
# In that case, the cache will record the generator Model object, and the list which we return will have
# both the generator Model object and the text chat Model object. That's fine because the cache is
# only used for check_model_availability() anyway.
self._model_cache[provider_resource_id] = model
models.append(model)
return models
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