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/remote/vector_io/qdrant/__init__.py

@@ -12,7 +12,11 @@ from .config import QdrantVectorIOConfig
 async def get_adapter_impl(config: QdrantVectorIOConfig, deps: dict[Api, ProviderSpec]):
     from .qdrant import QdrantVectorIOAdapter
 
-    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

llama_stack/providers/remote/vector_io/qdrant/qdrant.py

@@ -15,7 +15,8 @@ from qdrant_client.models import PointStruct
 
 from llama_stack.apis.common.errors import VectorStoreNotFoundError
 from llama_stack.apis.files import Files
-from llama_stack.apis.inference import InterleavedContent
+from llama_stack.apis.inference import Inference, InterleavedContent
+from llama_stack.apis.models import Models
 from llama_stack.apis.vector_dbs import VectorDB
 from llama_stack.apis.vector_io import (
     Chunk,
@@ -25,7 +26,7 @@ from llama_stack.apis.vector_io import (
     VectorStoreFileObject,
 )
 from llama_stack.log import get_logger
-from llama_stack.providers.datatypes import Api, VectorDBsProtocolPrivate
+from llama_stack.providers.datatypes import VectorDBsProtocolPrivate
 from llama_stack.providers.inline.vector_io.qdrant import QdrantVectorIOConfig as InlineQdrantVectorIOConfig
 from llama_stack.providers.utils.kvstore import kvstore_impl
 from llama_stack.providers.utils.memory.openai_vector_store_mixin import OpenAIVectorStoreMixin
@@ -159,7 +160,8 @@ class QdrantVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
     def __init__(
         self,
         config: RemoteQdrantVectorIOConfig | InlineQdrantVectorIOConfig,
-        inference_api: Api.inference,
+        inference_api: Inference,
+        models_api: Models,
         files_api: Files | None = None,
     ) -> None:
         super().__init__(files_api=files_api, kvstore=None)
@@ -167,6 +169,7 @@ class QdrantVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
         self.client: AsyncQdrantClient = None
         self.cache = {}
         self.inference_api = inference_api
+        self.models_api = models_api
         self.vector_db_store = None
         self._qdrant_lock = asyncio.Lock()