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/inline/vector_io/qdrant/__init__.py

@@ -15,7 +15,11 @@ async def get_provider_impl(config: QdrantVectorIOConfig, deps: dict[Api, Any]):
     from llama_stack.providers.remote.vector_io.qdrant.qdrant import QdrantVectorIOAdapter
 
     assert isinstance(config, QdrantVectorIOConfig), f"Unexpected config type: {type(config)}"
-    files_api = deps.get(Api.files)
-    impl = QdrantVectorIOAdapter(config, deps[Api.inference], files_api)
+    impl = QdrantVectorIOAdapter(
+        config,
+        deps[Api.inference],
+        deps[Api.models],
+        deps.get(Api.files),
+    )
     await impl.initialize()
     return impl