fix nvidia provider

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

@@ -5,15 +5,15 @@
 # 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.model_registry import ModelRegistryHelper
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
+from openai import NOT_GIVEN
 
 from . import NVIDIAConfig
 from .utils import _is_nvidia_hosted
@@ -21,9 +21,7 @@ from .utils import _is_nvidia_hosted
 logger = get_logger(name=__name__, category="inference::nvidia")
 
 
-class NVIDIAInferenceAdapter(OpenAIMixin):
+class NVIDIAInferenceAdapter(OpenAIMixin, ModelRegistryHelper):
-    config: NVIDIAConfig
-
     """
     NVIDIA Inference Adapter for Llama Stack.
 
@@ -37,12 +35,29 @@ class NVIDIAInferenceAdapter(OpenAIMixin):
     - ModelRegistryHelper.check_model_availability() just returns False and shows a warning
     """
 
+    def __init__(self, config: NVIDIAConfig) -> None:
+        """Initialize the NVIDIA inference adapter with configuration."""
+        # Initialize ModelRegistryHelper with empty model entries since NVIDIA uses dynamic model discovery
+        ModelRegistryHelper.__init__(
+            self, model_entries=[], allowed_models=config.allowed_models
+        )
+        self.config = config
+
     # source: https://docs.nvidia.com/nim/nemo-retriever/text-embedding/latest/support-matrix.html
     embedding_model_metadata: dict[str, dict[str, int]] = {
-        "nvidia/llama-3.2-nv-embedqa-1b-v2": {"embedding_dimension": 2048, "context_length": 8192},
+        "nvidia/llama-3.2-nv-embedqa-1b-v2": {
+            "embedding_dimension": 2048,
+            "context_length": 8192,
+        },
         "nvidia/nv-embedqa-e5-v5": {"embedding_dimension": 512, "context_length": 1024},
-        "nvidia/nv-embedqa-mistral-7b-v2": {"embedding_dimension": 512, "context_length": 4096},
+        "nvidia/nv-embedqa-mistral-7b-v2": {
-        "snowflake/arctic-embed-l": {"embedding_dimension": 512, "context_length": 1024},
+            "embedding_dimension": 512,
+            "context_length": 4096,
+        },
+        "snowflake/arctic-embed-l": {
+            "embedding_dimension": 512,
+            "context_length": 1024,
+        },
     }
 
     async def initialize(self) -> None:
@@ -60,7 +75,9 @@ class NVIDIAInferenceAdapter(OpenAIMixin):
 
         :return: The NVIDIA API key
         """
-        return self.config.api_key.get_secret_value() if self.config.api_key else "NO KEY"
+        return (
+            self.config.api_key.get_secret_value() if self.config.api_key else "NO KEY"
+        )
 
     def get_base_url(self) -> str:
         """
@@ -68,7 +85,11 @@ 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
+        return (
+            f"{self.config.url}/v1"
+            if self.config.append_api_version
+            else self.config.url
+        )
 
     async def openai_embeddings(
         self,
@@ -95,7 +116,9 @@ class NVIDIAInferenceAdapter(OpenAIMixin):
         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,
+            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,