add embedding model by default to distribution templates (#617)

# What does this PR do?
Adds the sentence transformer provider and the `all-MiniLM-L6-v2`
embedding model to the default models to register in the run.yaml for
all providers.

## Test Plan
llama stack build --template together --image-type conda
llama stack run
~/.llama/distributions/llamastack-together/together-run.yaml

llama_stack/providers/tests/inference/fixtures.py

@@ -238,7 +238,7 @@ async def inference_stack(request, inference_model):
     model_type = ModelType.llm
     metadata = {}
     if os.getenv("EMBEDDING_DIMENSION"):
-        model_type = ModelType.embedding_model
+        model_type = ModelType.embedding
         metadata["embedding_dimension"] = get_env_or_fail("EMBEDDING_DIMENSION")
 
     test_stack = await construct_stack_for_test(

llama_stack/providers/tests/inference/test_embeddings.py

@@ -18,7 +18,7 @@ class TestEmbeddings:
         inference_impl, models_impl = inference_stack
         model = await models_impl.get_model(inference_model)
 
-        if model.model_type != ModelType.embedding_model:
+        if model.model_type != ModelType.embedding:
             pytest.skip("This test is only applicable for embedding models")
 
         response = await inference_impl.embeddings(
@@ -39,7 +39,7 @@ class TestEmbeddings:
         inference_impl, models_impl = inference_stack
         model = await models_impl.get_model(inference_model)
 
-        if model.model_type != ModelType.embedding_model:
+        if model.model_type != ModelType.embedding:
             pytest.skip("This test is only applicable for embedding models")
 
         texts = ["Hello, world!", "This is a test", "Testing embeddings"]

llama_stack/providers/tests/memory/fixtures.py

@@ -125,7 +125,7 @@ async def memory_stack(inference_model, request):
         models=[
             ModelInput(
                 model_id=inference_model,
-                model_type=ModelType.embedding_model,
+                model_type=ModelType.embedding,
                 metadata={
                     "embedding_dimension": get_env_or_fail("EMBEDDING_DIMENSION"),
                 },