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/inline/inference/meta_reference/inference.py

@@ -83,7 +83,7 @@ class MetaReferenceInferenceImpl(
 
     async def register_model(self, model: Model) -> Model:
         model = await self.model_registry_helper.register_model(model)
-        if model.model_type == ModelType.embedding_model:
+        if model.model_type == ModelType.embedding:
             self._load_sentence_transformer_model(model.provider_resource_id)
         return model
 

llama_stack/providers/inline/inference/sentence_transformers/config.py

@@ -4,7 +4,13 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
+from typing import Any, Dict
+
 from pydantic import BaseModel
 
 
-class SentenceTransformersInferenceConfig(BaseModel): ...
+class SentenceTransformersInferenceConfig(BaseModel):
+
+    @classmethod
+    def sample_run_config(cls) -> Dict[str, Any]:
+        return {}

llama_stack/providers/remote/inference/ollama/ollama.py

@@ -337,7 +337,7 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
 
     async def register_model(self, model: Model) -> Model:
         # ollama does not have embedding models running. Check if the model is in list of available models.
-        if model.model_type == ModelType.embedding_model:
+        if model.model_type == ModelType.embedding:
             response = await self.client.list()
             available_models = [m["model"] for m in response["models"]]
             if model.provider_resource_id not in available_models:

llama_stack/providers/remote/inference/vllm/vllm.py

@@ -207,7 +207,7 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
         model = await self.model_store.get_model(model_id)
 
         kwargs = {}
-        assert model.model_type == ModelType.embedding_model
+        assert model.model_type == ModelType.embedding
         assert model.metadata.get("embedding_dimensions")
         kwargs["dimensions"] = model.metadata.get("embedding_dimensions")
         assert all(

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"),
                 },

llama_stack/providers/utils/inference/model_registry.py

@@ -78,7 +78,7 @@ class ModelRegistryHelper(ModelsProtocolPrivate):
             return None
 
     async def register_model(self, model: Model) -> Model:
-        if model.model_type == ModelType.embedding_model:
+        if model.model_type == ModelType.embedding:
             # embedding models are always registered by their provider model id and does not need to be mapped to a llama model
             provider_resource_id = model.provider_resource_id
         else: