Make embedding generation go through inference (#606)

This PR does the following:
1) adds the ability to generate embeddings in all supported inference
providers.
2) Moves all the memory providers to use the inference API and improved
the memory tests to setup the inference stack correctly and use the
embedding models

This is a merge from #589 and #598

llama_stack/providers/inline/inference/meta_reference/inference.py

@@ -16,12 +16,14 @@ from llama_models.llama3.api.datatypes import *  # noqa: F403
 from llama_stack.providers.utils.inference.model_registry import build_model_alias
 from llama_stack.apis.inference import *  # noqa: F403
 from llama_stack.providers.datatypes import ModelsProtocolPrivate
+from llama_stack.providers.utils.inference.embedding_mixin import (
+    SentenceTransformerEmbeddingMixin,
+)
 from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
 from llama_stack.providers.utils.inference.prompt_adapter import (
     convert_image_media_to_url,
     request_has_media,
 )
-
 from .config import MetaReferenceInferenceConfig
 from .generation import Llama
 from .model_parallel import LlamaModelParallelGenerator
@@ -32,12 +34,17 @@ log = logging.getLogger(__name__)
 SEMAPHORE = asyncio.Semaphore(1)
 
 
-class MetaReferenceInferenceImpl(Inference, ModelRegistryHelper, ModelsProtocolPrivate):
+class MetaReferenceInferenceImpl(
+    SentenceTransformerEmbeddingMixin,
+    Inference,
+    ModelsProtocolPrivate,
+):
     def __init__(self, config: MetaReferenceInferenceConfig) -> None:
         self.config = config
         model = resolve_model(config.model)
-        ModelRegistryHelper.__init__(
-            self,
+        if model is None:
+            raise RuntimeError(f"Unknown model: {config.model}, Run `llama model list`")
+        self.model_registry_helper = ModelRegistryHelper(
             [
                 build_model_alias(
                     model.descriptor(),
@@ -45,8 +52,6 @@ class MetaReferenceInferenceImpl(Inference, ModelRegistryHelper, ModelsProtocolP
                 )
             ],
         )
-        if model is None:
-            raise RuntimeError(f"Unknown model: {config.model}, Run `llama model list`")
         self.model = model
         # verify that the checkpoint actually is for this model lol
 
@@ -76,6 +81,12 @@ class MetaReferenceInferenceImpl(Inference, ModelRegistryHelper, ModelsProtocolP
     async def unregister_model(self, model_id: str) -> None:
         pass
 
+    async def register_model(self, model: Model) -> Model:
+        model = await self.model_registry_helper.register_model(model)
+        if model.model_type == ModelType.embedding_model:
+            self._load_sentence_transformer_model(model.provider_resource_id)
+        return model
+
     async def completion(
         self,
         model_id: str,
@@ -394,13 +405,6 @@ class MetaReferenceInferenceImpl(Inference, ModelRegistryHelper, ModelsProtocolP
             for x in impl():
                 yield x
 
-    async def embeddings(
-        self,
-        model_id: str,
-        contents: List[InterleavedTextMedia],
-    ) -> EmbeddingsResponse:
-        raise NotImplementedError()
-
 
 async def request_with_localized_media(
     request: Union[ChatCompletionRequest, CompletionRequest],