Merge origin/main into add-missing-provider-data-impls

Resolved conflicts in:
- benchmarking/k8s-benchmark/stack_run_config.yaml (accepted new storage schema)
- llama_stack/providers/remote/inference/cerebras/cerebras.py (kept provider data support)
- llama_stack/providers/remote/inference/cerebras/config.py (kept provider data support)
- llama_stack/providers/remote/inference/nvidia/config.py (kept provider data support)
- llama_stack/providers/remote/inference/runpod/config.py (merged imports)
- pyproject.toml (kept databricks-sdk dependency)

llama_stack/providers/remote/inference/nvidia/nvidia.py

@@ -5,13 +5,6 @@
 # the root directory of this source tree.
 
 
-from openai import NOT_GIVEN
-
-from llama_stack.apis.inference import (
-    OpenAIEmbeddingData,
-    OpenAIEmbeddingsResponse,
-    OpenAIEmbeddingUsage,
-)
 from llama_stack.log import get_logger
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 
@@ -28,15 +21,6 @@ class NVIDIAInferenceAdapter(OpenAIMixin):
 
     """
     NVIDIA Inference Adapter for Llama Stack.
-
-    Note: The inheritance order is important here. OpenAIMixin must come before
-    ModelRegistryHelper to ensure that OpenAIMixin.check_model_availability()
-    is used instead of ModelRegistryHelper.check_model_availability(). It also
-    must come before Inference to ensure that OpenAIMixin methods are available
-    in the Inference interface.
-
-    - OpenAIMixin.check_model_availability() queries the NVIDIA API to check if a model exists
-    - ModelRegistryHelper.check_model_availability() just returns False and shows a warning
     """
 
     # source: https://docs.nvidia.com/nim/nemo-retriever/text-embedding/latest/support-matrix.html
@@ -51,7 +35,7 @@ class NVIDIAInferenceAdapter(OpenAIMixin):
         logger.info(f"Initializing NVIDIAInferenceAdapter({self.config.url})...")
 
         if _is_nvidia_hosted(self.config):
-            if not self.config.api_key:
+            if not self.config.auth_credential:
                 raise RuntimeError(
                     "API key is required for hosted NVIDIA NIM. Either provide an API key or use a self-hosted NIM."
                 )
@@ -62,7 +46,13 @@ class NVIDIAInferenceAdapter(OpenAIMixin):
 
         :return: The NVIDIA API key
         """
-        return self.config.api_key.get_secret_value() if self.config.api_key else "NO KEY"
+        if self.config.auth_credential:
+            return self.config.auth_credential.get_secret_value()
+
+        if not _is_nvidia_hosted(self.config):
+            return "NO KEY REQUIRED"
+
+        return None
 
     def get_base_url(self) -> str:
         """
@@ -71,54 +61,3 @@ class NVIDIAInferenceAdapter(OpenAIMixin):
         :return: The NVIDIA API base URL
         """
         return f"{self.config.url}/v1" if self.config.append_api_version else self.config.url
-
-    async def openai_embeddings(
-        self,
-        model: str,
-        input: str | list[str],
-        encoding_format: str | None = "float",
-        dimensions: int | None = None,
-        user: str | None = None,
-    ) -> OpenAIEmbeddingsResponse:
-        """
-        OpenAI-compatible embeddings for NVIDIA NIM.
-
-        Note: NVIDIA NIM asymmetric embedding models require an "input_type" field not present in the standard OpenAI embeddings API.
-        We default this to "query" to ensure requests succeed when using the
-        OpenAI-compatible endpoint. For passage embeddings, use the embeddings API with
-        `task_type='document'`.
-        """
-        extra_body: dict[str, object] = {"input_type": "query"}
-        logger.warning(
-            "NVIDIA OpenAI-compatible embeddings: defaulting to input_type='query'. "
-            "For passage embeddings, use the embeddings API with task_type='document'."
-        )
-
-        response = await self.client.embeddings.create(
-            model=await self._get_provider_model_id(model),
-            input=input,
-            encoding_format=encoding_format if encoding_format is not None else NOT_GIVEN,
-            dimensions=dimensions if dimensions is not None else NOT_GIVEN,
-            user=user if user is not None else NOT_GIVEN,
-            extra_body=extra_body,
-        )
-
-        data = []
-        for i, embedding_data in enumerate(response.data):
-            data.append(
-                OpenAIEmbeddingData(
-                    embedding=embedding_data.embedding,
-                    index=i,
-                )
-            )
-
-        usage = OpenAIEmbeddingUsage(
-            prompt_tokens=response.usage.prompt_tokens,
-            total_tokens=response.usage.total_tokens,
-        )
-
-        return OpenAIEmbeddingsResponse(
-            data=data,
-            model=response.model,
-            usage=usage,
-        )