feat: Enable setting a default embedding model in the stack

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

llama_stack/providers/remote/vector_io/milvus/__init__.py

@@ -14,6 +14,11 @@ async def get_adapter_impl(config: MilvusVectorIOConfig, deps: dict[Api, Provide
 
     assert isinstance(config, MilvusVectorIOConfig), f"Unexpected config type: {type(config)}"
 
-    impl = MilvusVectorIOAdapter(config, deps[Api.inference], deps.get(Api.files, None))
+    impl = MilvusVectorIOAdapter(
+        config,
+        deps[Api.inference],
+        deps[Api.models],
+        deps.get(Api.files),
+    )
     await impl.initialize()
     return impl

llama_stack/providers/remote/vector_io/milvus/milvus.py

@@ -12,8 +12,9 @@ from numpy.typing import NDArray
 from pymilvus import AnnSearchRequest, DataType, Function, FunctionType, MilvusClient, RRFRanker, WeightedRanker
 
 from llama_stack.apis.common.errors import VectorStoreNotFoundError
-from llama_stack.apis.files.files import Files
+from llama_stack.apis.files import Files
 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,
@@ -307,6 +308,7 @@ class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
         self,
         config: RemoteMilvusVectorIOConfig | InlineMilvusVectorIOConfig,
         inference_api: Inference,
+        models_api: Models,
         files_api: Files | None,
     ) -> None:
         super().__init__(files_api=files_api, kvstore=None)
@@ -314,6 +316,7 @@ class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
         self.cache = {}
         self.client = None
         self.inference_api = inference_api
+        self.models_api = models_api
         self.vector_db_store = None
         self.metadata_collection_name = "openai_vector_stores_metadata"