feat: add static embedding metadata to dynamic model listings for providers using OpenAIMixin (#3547)

# What does this PR do?

- remove auto-download of ollama embedding models
- add embedding model metadata to dynamic listing w/ unit test
- add support and tests for allowed_models
- removed inference provider models.py files where dynamic listing is
enabled
- store embedding metadata in embedding_model_metadata field on
inference providers
- make model_entries optional on ModelRegistryHelper and
LiteLLMOpenAIMixin
- make OpenAIMixin a ModelRegistryHelper
- skip base64 embedding test for remote::ollama, always returns floats
- only use OpenAI client for ollama model listing
- remove unused build_model_entry function
- remove unused get_huggingface_repo function


## Test Plan

ci w/ new tests

llama_stack/providers/utils/inference/openai_mixin.py

@@ -24,12 +24,13 @@ from llama_stack.apis.inference import (
 )
 from llama_stack.apis.models import ModelType
 from llama_stack.log import get_logger
+from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
 from llama_stack.providers.utils.inference.openai_compat import prepare_openai_completion_params
 
 logger = get_logger(name=__name__, category="providers::utils")
 
 
-class OpenAIMixin(ABC):
+class OpenAIMixin(ModelRegistryHelper, ABC):
     """
     Mixin class that provides OpenAI-specific functionality for inference providers.
     This class handles direct OpenAI API calls using the AsyncOpenAI client.
@@ -50,10 +51,18 @@ class OpenAIMixin(ABC):
     # This is useful for providers that do not return a unique id in the response.
     overwrite_completion_id: bool = False
 
+    # Embedding model metadata for this provider
+    # Can be set by subclasses or instances to provide embedding models
+    # Format: {"model_id": {"embedding_dimension": 1536, "context_length": 8192}}
+    embedding_model_metadata: dict[str, dict[str, int]] = {}
+
     # Cache of available models keyed by model ID
     # This is set in list_models() and used in check_model_availability()
     _model_cache: dict[str, Model] = {}
 
+    # List of allowed models for this provider, if empty all models allowed
+    allowed_models: list[str] = []
+
     @abstractmethod
     def get_api_key(self) -> str:
         """
@@ -302,22 +311,36 @@ class OpenAIMixin(ABC):
 
     async def list_models(self) -> list[Model] | None:
         """
-        List available models from the provider's /v1/models endpoint.
+        List available models from the provider's /v1/models endpoint augmented with static embedding model metadata.
 
         Also, caches the models in self._model_cache for use in check_model_availability().
 
         :return: A list of Model instances representing available models.
         """
-        self._model_cache = {
-            m.id: Model(
-                # __provider_id__ is dynamically added by instantiate_provider in resolver.py
-                provider_id=self.__provider_id__,  # type: ignore[attr-defined]
-                provider_resource_id=m.id,
-                identifier=m.id,
-                model_type=ModelType.llm,
-            )
-            async for m in self.client.models.list()
-        }
+        self._model_cache = {}
+
+        async for m in self.client.models.list():
+            if self.allowed_models and m.id not in self.allowed_models:
+                logger.info(f"Skipping model {m.id} as it is not in the allowed models list")
+                continue
+            if metadata := self.embedding_model_metadata.get(m.id):
+                # This is an embedding model - augment with metadata
+                model = Model(
+                    provider_id=self.__provider_id__,  # type: ignore[attr-defined]
+                    provider_resource_id=m.id,
+                    identifier=m.id,
+                    model_type=ModelType.embedding,
+                    metadata=metadata,
+                )
+            else:
+                # This is an LLM
+                model = Model(
+                    provider_id=self.__provider_id__,  # type: ignore[attr-defined]
+                    provider_resource_id=m.id,
+                    identifier=m.id,
+                    model_type=ModelType.llm,
+                )
+            self._model_cache[m.id] = model
 
         return list(self._model_cache.values())