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>

tests/unit/providers/vector_io/test_faiss.py

@@ -11,6 +11,7 @@ import numpy as np
 import pytest
 
 from llama_stack.apis.files import Files
+from llama_stack.apis.models import Models
 from llama_stack.apis.vector_dbs import VectorDB
 from llama_stack.apis.vector_io import Chunk, QueryChunksResponse
 from llama_stack.providers.datatypes import HealthStatus
@@ -75,6 +76,12 @@ def mock_files_api():
     return mock_api
 
 
+@pytest.fixture
+def mock_models_api():
+    mock_api = MagicMock(spec=Models)
+    return mock_api
+
+
 @pytest.fixture
 def faiss_config():
     config = MagicMock(spec=FaissVectorIOConfig)
@@ -110,7 +117,7 @@ async def test_faiss_query_vector_returns_infinity_when_query_and_embedding_are_
         assert response.chunks[1] == sample_chunks[1]
 
 
-async def test_health_success():
+async def test_health_success(mock_models_api):
     """Test that the health check returns OK status when faiss is working correctly."""
     # Create a fresh instance of FaissVectorIOAdapter for testing
     config = MagicMock()
@@ -119,7 +126,9 @@ async def test_health_success():
 
     with patch("llama_stack.providers.inline.vector_io.faiss.faiss.faiss.IndexFlatL2") as mock_index_flat:
         mock_index_flat.return_value = MagicMock()
-        adapter = FaissVectorIOAdapter(config=config, inference_api=inference_api, files_api=files_api)
+        adapter = FaissVectorIOAdapter(
+            config=config, inference_api=inference_api, models_api=mock_models_api, files_api=files_api
+        )
 
         # Calling the health method directly
         response = await adapter.health()
@@ -133,7 +142,7 @@ async def test_health_success():
         mock_index_flat.assert_called_once_with(128)  # VECTOR_DIMENSION is 128
 
 
-async def test_health_failure():
+async def test_health_failure(mock_models_api):
     """Test that the health check returns ERROR status when faiss encounters an error."""
     # Create a fresh instance of FaissVectorIOAdapter for testing
     config = MagicMock()
@@ -143,7 +152,9 @@ async def test_health_failure():
     with patch("llama_stack.providers.inline.vector_io.faiss.faiss.faiss.IndexFlatL2") as mock_index_flat:
         mock_index_flat.side_effect = Exception("Test error")
 
-        adapter = FaissVectorIOAdapter(config=config, inference_api=inference_api, files_api=files_api)
+        adapter = FaissVectorIOAdapter(
+            config=config, inference_api=inference_api, models_api=mock_models_api, files_api=files_api
+        )
 
         # Calling the health method directly
         response = await adapter.health()