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Merge branch 'main' into add-mcp-authentication-param
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commit
411b18a90f
515 changed files with 1213 additions and 370490 deletions
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@ -283,8 +283,8 @@ class WatsonXInferenceAdapter(LiteLLMOpenAIMixin):
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# ...
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provider_resource_id = f"{self.__provider_id__}/{model_spec['model_id']}"
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if "embedding" in functions:
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embedding_dimension = model_spec["model_limits"]["embedding_dimension"]
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context_length = model_spec["model_limits"]["max_sequence_length"]
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embedding_dimension = model_spec.get("model_limits", {}).get("embedding_dimension", 0)
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context_length = model_spec.get("model_limits", {}).get("max_sequence_length", 0)
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embedding_metadata = {
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"embedding_dimension": embedding_dimension,
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"context_length": context_length,
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@ -306,10 +306,6 @@ class WatsonXInferenceAdapter(LiteLLMOpenAIMixin):
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metadata={},
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model_type=ModelType.llm,
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)
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# In theory, I guess it is possible that a model could be both an embedding model and a text chat model.
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# In that case, the cache will record the generator Model object, and the list which we return will have
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# both the generator Model object and the text chat Model object. That's fine because the cache is
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# only used for check_model_availability() anyway.
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self._model_cache[provider_resource_id] = model
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models.append(model)
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return models
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