incorporating feedback

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>

llama_stack/core/stack.py

@@ -98,6 +98,30 @@ REGISTRY_REFRESH_TASK = None
 TEST_RECORDING_CONTEXT = None
 
 
+async def validate_default_embedding_model(impls: dict[Api, Any]):
+    """Validate that at most one embedding model is marked as default."""
+    if Api.models not in impls:
+        return
+
+    models_impl = impls[Api.models]
+    response = await models_impl.list_models()
+    models_list = response.data if hasattr(response, "data") else response
+
+    default_embedding_models = []
+    for model in models_list:
+        if model.model_type == "embedding" and model.metadata.get("default_configured") is True:
+            default_embedding_models.append(model.identifier)
+
+    if len(default_embedding_models) > 1:
+        raise ValueError(
+            f"Multiple embedding models marked as default_configured=True: {default_embedding_models}. "
+            "Only one embedding model can be marked as default."
+        )
+
+    if default_embedding_models:
+        logger.info(f"Default embedding model configured: {default_embedding_models[0]}")
+
+
 async def register_resources(run_config: StackRunConfig, impls: dict[Api, Any]):
     for rsrc, api, register_method, list_method in RESOURCES:
         objects = getattr(run_config, rsrc)
@@ -128,6 +152,8 @@ async def register_resources(run_config: StackRunConfig, impls: dict[Api, Any]):
                 f"{rsrc.capitalize()}: {obj.identifier} served by {obj.provider_id}",
             )
 
+    await validate_default_embedding_model(impls)
+
 
 class EnvVarError(Exception):
     def __init__(self, var_name: str, path: str = ""):

llama_stack/providers/utils/memory/openai_vector_store_mixin.py

@@ -501,25 +501,24 @@ class OpenAIVectorStoreMixin(ABC):
         """
         embedding_models = await self._get_embedding_models()
 
-        default_model_info = []
+        default_models = []
         for model in embedding_models:
             if model.metadata.get("default_configured") is True:
-                embedding_dimension = model.metadata.get("embedding_dimension")
-                if embedding_dimension is None:
-                    raise ValueError(f"Embedding model '{model.identifier}' has no embedding_dimension in metadata")
-                default_model_info.append((model.identifier, int(embedding_dimension)))
+                default_models.append(model.identifier)
 
-        if len(default_model_info) > 1:
-            model_ids = [info[0] for info in default_model_info]
+        if len(default_models) > 1:
             raise ValueError(
-                f"Multiple embedding models marked as default_configured=True: {model_ids}. "
+                f"Multiple embedding models marked as default_configured=True: {default_models}. "
                 "Only one embedding model can be marked as default."
             )
 
-        if default_model_info:
-            model_id, dimension = default_model_info[0]
-            logger.info(f"Using default embedding model: {model_id} with dimension {dimension}")
-            return model_id, dimension
+        if default_models:
+            model_id = default_models[0]
+            embedding_dimension = await self._get_embedding_dimension_for_model(model_id)
+            if embedding_dimension is None:
+                raise ValueError(f"Embedding model '{model_id}' has no embedding_dimension in metadata")
+            logger.info(f"Using default embedding model: {model_id} with dimension {embedding_dimension}")
+            return model_id, embedding_dimension
 
         logger.info("DEBUG: No default embedding models found")
         return None