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/core/routers/vector_io.py

@@ -120,13 +120,7 @@ class VectorIORouter(VectorIO):
         embedding_dimension = extra.get("embedding_dimension")
         provider_id = extra.get("provider_id")
 
-        logger.debug(f"VectorIORouter.openai_create_vector_store: name={params.name}, provider_id={provider_id}")
-
-        # Require explicit embedding model specification
-        if embedding_model is None:
-            raise ValueError("embedding_model is required in extra_body when creating a vector store")
-
-        if embedding_dimension is None:
+        if embedding_model is not None and embedding_dimension is None:
             embedding_dimension = await self._get_embedding_model_dimension(embedding_model)
 
         # Auto-select provider if not specified
@@ -158,8 +152,10 @@ class VectorIORouter(VectorIO):
             params.model_extra = {}
         params.model_extra["provider_vector_db_id"] = registered_vector_db.provider_resource_id
         params.model_extra["provider_id"] = registered_vector_db.provider_id
-        params.model_extra["embedding_model"] = embedding_model
-        params.model_extra["embedding_dimension"] = embedding_dimension
+        if embedding_model is not None:
+            params.model_extra["embedding_model"] = embedding_model
+        if embedding_dimension is not None:
+            params.model_extra["embedding_dimension"] = embedding_dimension
 
         return await provider.openai_create_vector_store(params)