add model type to APIs (#588)

# What does this PR do?

This PR adds a new model type field to support embedding models to be
registered. Summary of changes:
1) Each registered model by default is an llm model. 
2) User can specify an embedding model type, while registering.If
specified, the model bypass the llama model checks since embedding
models can by of any type and based on llama.
3) User needs to include the required embedding dimension in metadata.
This will be used by embedding generation to generate the requried size
of embeddings.


## Test Plan

This PR will go together will need to be merged with two follow up PRs
that will include test plans.

llama_stack/distribution/routers/routing_tables.py

@@ -209,6 +209,7 @@ class ModelsRoutingTable(CommonRoutingTableImpl, Models):
         provider_model_id: Optional[str] = None,
         provider_id: Optional[str] = None,
         metadata: Optional[Dict[str, Any]] = None,
+        model_type: Optional[ModelType] = None,
     ) -> Model:
         if provider_model_id is None:
             provider_model_id = model_id
@@ -222,11 +223,21 @@ class ModelsRoutingTable(CommonRoutingTableImpl, Models):
                 )
         if metadata is None:
             metadata = {}
+        if model_type is None:
+            model_type = ModelType.llm
+        if (
+            "embedding_dimension" not in metadata
+            and model_type == ModelType.embedding_model
+        ):
+            raise ValueError(
+                "Embedding model must have an embedding dimension in its metadata"
+            )
         model = Model(
             identifier=model_id,
             provider_resource_id=provider_model_id,
             provider_id=provider_id,
             metadata=metadata,
+            model_type=model_type,
         )
         registered_model = await self.register_object(model)
         return registered_model
@@ -298,16 +309,29 @@ class MemoryBanksRoutingTable(CommonRoutingTableImpl, MemoryBanks):
                 raise ValueError(
                     "No provider specified and multiple providers available. Please specify a provider_id."
                 )
-        memory_bank = parse_obj_as(
-            MemoryBank,
-            {
-                "identifier": memory_bank_id,
-                "type": ResourceType.memory_bank.value,
-                "provider_id": provider_id,
-                "provider_resource_id": provider_memory_bank_id,
-                **params.model_dump(),
-            },
-        )
+        model = await self.get_object_by_identifier("model", params.embedding_model)
+        if model is None:
+            raise ValueError(f"Model {params.embedding_model} not found")
+        if model.model_type != ModelType.embedding_model:
+            raise ValueError(
+                f"Model {params.embedding_model} is not an embedding model"
+            )
+        if "embedding_dimension" not in model.metadata:
+            raise ValueError(
+                f"Model {params.embedding_model} does not have an embedding dimension"
+            )
+        memory_bank_data = {
+            "identifier": memory_bank_id,
+            "type": ResourceType.memory_bank.value,
+            "provider_id": provider_id,
+            "provider_resource_id": provider_memory_bank_id,
+            **params.model_dump(),
+        }
+        if params.memory_bank_type == MemoryBankType.vector.value:
+            memory_bank_data["embedding_dimension"] = model.metadata[
+                "embedding_dimension"
+            ]
+        memory_bank = parse_obj_as(MemoryBank, memory_bank_data)
         await self.register_object(memory_bank)
         return memory_bank