feat: Enable setting a default embedding model in the stack (#3803)

# What does this PR do?

Enables automatic embedding model detection for vector stores and by
using a `default_configured` boolean that can be defined in the
`run.yaml`.

<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->

## Test Plan
- Unit tests
- Integration tests
- Simple example below:

Spin up the stack:
```bash
uv run llama stack build --distro starter --image-type venv --run
```
Then test with OpenAI's client:
```python
from openai import OpenAI
client = OpenAI(base_url="http://localhost:8321/v1/", api_key="none")
vs = client.vector_stores.create()
```
Previously you needed:

```python
vs = client.vector_stores.create(
    extra_body={
        "embedding_model": "sentence-transformers/all-MiniLM-L6-v2",
        "embedding_dimension": 384,
    }
)
```

The `extra_body` is now unnecessary.

---------

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

llama_stack/providers/utils/memory/openai_vector_store_mixin.py

@@ -17,6 +17,7 @@ from pydantic import TypeAdapter
 
 from llama_stack.apis.common.errors import VectorStoreNotFoundError
 from llama_stack.apis.files import Files, OpenAIFileObject
+from llama_stack.apis.models import Model, Models
 from llama_stack.apis.vector_dbs import VectorDB
 from llama_stack.apis.vector_io import (
     Chunk,
@@ -77,11 +78,14 @@ class OpenAIVectorStoreMixin(ABC):
 
     # Implementing classes should call super().__init__() in their __init__ method
     # to properly initialize the mixin attributes.
-    def __init__(self, files_api: Files | None = None, kvstore: KVStore | None = None):
+    def __init__(
+        self, files_api: Files | None = None, kvstore: KVStore | None = None, models_api: Models | None = None
+    ):
         self.openai_vector_stores: dict[str, dict[str, Any]] = {}
         self.openai_file_batches: dict[str, dict[str, Any]] = {}
         self.files_api = files_api
         self.kvstore = kvstore
+        self.models_api = models_api
         self._last_file_batch_cleanup_time = 0
         self._file_batch_tasks: dict[str, asyncio.Task[None]] = {}
 
@@ -348,20 +352,32 @@ class OpenAIVectorStoreMixin(ABC):
         """Creates a vector store."""
         created_at = int(time.time())
 
-        # Extract llama-stack-specific parameters from extra_body
         extra = params.model_extra or {}
         provider_vector_db_id = extra.get("provider_vector_db_id")
         embedding_model = extra.get("embedding_model")
-        embedding_dimension = extra.get("embedding_dimension", 768)
+        embedding_dimension = extra.get("embedding_dimension")
         # use provider_id set by router; fallback to provider's own ID when used directly via --stack-config
         provider_id = extra.get("provider_id") or getattr(self, "__provider_id__", None)
         # Derive the canonical vector_db_id (allow override, else generate)
         vector_db_id = provider_vector_db_id or generate_object_id("vector_store", lambda: f"vs_{uuid.uuid4()}")
 
         if embedding_model is None:
-            raise ValueError("Embedding model is required")
+            result = await self._get_default_embedding_model_and_dimension()
+            if result is None:
+                raise ValueError(
+                    "embedding_model is required in extra_body when creating a vector store. "
+                    "No default embedding model could be determined automatically."
+                )
+            embedding_model, embedding_dimension = result
+        elif embedding_dimension is None:
+            # Embedding model was provided but dimension wasn't, look it up
+            embedding_dimension = await self._get_embedding_dimension_for_model(embedding_model)
+            if embedding_dimension is None:
+                raise ValueError(
+                    f"Could not determine embedding dimension for model '{embedding_model}'. "
+                    "Please provide embedding_dimension in extra_body or ensure the model metadata contains embedding_dimension."
+                )
 
-        # Embedding dimension is required (defaulted to 768 if not provided)
         if embedding_dimension is None:
             raise ValueError("Embedding dimension is required")
 
@@ -428,6 +444,85 @@ class OpenAIVectorStoreMixin(ABC):
         store_info = self.openai_vector_stores[vector_db_id]
         return VectorStoreObject.model_validate(store_info)
 
+    async def _get_embedding_models(self) -> list[Model]:
+        """Get list of embedding models from the models API."""
+        if not self.models_api:
+            return []
+
+        models_response = await self.models_api.list_models()
+        models_list = models_response.data if hasattr(models_response, "data") else models_response
+
+        embedding_models = []
+        for model in models_list:
+            if not isinstance(model, Model):
+                logger.warning(f"Non-Model object found in models list: {type(model)} - {model}")
+                continue
+            if model.model_type == "embedding":
+                embedding_models.append(model)
+
+        return embedding_models
+
+    async def _get_embedding_dimension_for_model(self, model_id: str) -> int | None:
+        """Get embedding dimension for a specific model by looking it up in the models API.
+
+        Args:
+            model_id: The identifier of the embedding model (supports both prefixed and non-prefixed)
+
+        Returns:
+            The embedding dimension for the model, or None if not found
+        """
+        embedding_models = await self._get_embedding_models()
+
+        for model in embedding_models:
+            # Check for exact match first
+            if model.identifier == model_id:
+                embedding_dimension = model.metadata.get("embedding_dimension")
+                if embedding_dimension is not None:
+                    return int(embedding_dimension)
+                else:
+                    logger.warning(f"Model {model_id} found but has no embedding_dimension in metadata")
+                    return None
+
+            # Check for prefixed/unprefixed variations
+            # If model_id is unprefixed, check if it matches the resource_id
+            if model.provider_resource_id == model_id:
+                embedding_dimension = model.metadata.get("embedding_dimension")
+                if embedding_dimension is not None:
+                    return int(embedding_dimension)
+
+        return None
+
+    async def _get_default_embedding_model_and_dimension(self) -> tuple[str, int] | None:
+        """Get default embedding model from the models API.
+
+        Looks for embedding models marked with default_configured=True in metadata.
+        Returns None if no default embedding model is found.
+        Raises ValueError if multiple defaults are found.
+        """
+        embedding_models = await self._get_embedding_models()
+
+        default_models = []
+        for model in embedding_models:
+            if model.metadata.get("default_configured") is True:
+                default_models.append(model.identifier)
+
+        if len(default_models) > 1:
+            raise ValueError(
+                f"Multiple embedding models marked as default_configured=True: {default_models}. "
+                "Only one embedding model can be marked as default."
+            )
+
+        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
+
     async def openai_list_vector_stores(
         self,
         limit: int | None = 20,