feat: configure vector-io provider with an embedding model

Signed-off-by: Mustafa Elbehery <melbeher@redhat.com>

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

@@ -9,6 +9,7 @@ from typing import Any
 from pydantic import BaseModel, ConfigDict, Field
 
 from llama_stack.providers.utils.kvstore.config import KVStoreConfig, SqliteKVStoreConfig
+from llama_stack.providers.utils.vector_io.embedding_config import EmbeddingConfig
 from llama_stack.schema_utils import json_schema_type
 
 
@@ -18,6 +19,10 @@ class MilvusVectorIOConfig(BaseModel):
     token: str | None = Field(description="The token of the Milvus server")
     consistency_level: str = Field(description="The consistency level of the Milvus server", default="Strong")
     kvstore: KVStoreConfig = Field(description="Config for KV store backend")
+    embedding: EmbeddingConfig | None = Field(
+        default=None,
+        description="Default embedding configuration for this provider. When specified, vector databases created with this provider will use these embedding settings as defaults.",
+    )
 
     # This configuration allows additional fields to be passed through to the underlying Milvus client.
     # See the [Milvus](https://milvus.io/docs/install-overview.md) documentation for more details about Milvus in general.
@@ -32,4 +37,9 @@ class MilvusVectorIOConfig(BaseModel):
                 __distro_dir__=__distro_dir__,
                 db_name="milvus_remote_registry.db",
             ),
+            # Optional: Configure default embedding model for this provider
+            # "embedding": {
+            #     "model": "${env.MILVUS_EMBEDDING_MODEL:=all-MiniLM-L6-v2}",
+            #     "dimensions": 384
+            # },
         }

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

@@ -29,6 +29,7 @@ from llama_stack.providers.utils.memory.openai_vector_store_mixin import OpenAIV
 from llama_stack.providers.utils.memory.vector_store import (
     EmbeddingIndex,
     VectorDBWithIndex,
+    apply_provider_embedding_defaults,
 )
 from llama_stack.providers.utils.vector_io.vector_utils import sanitize_collection_name
 
@@ -305,6 +306,9 @@ class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
         self,
         vector_db: VectorDB,
     ) -> None:
+        # Apply provider-level embedding defaults if configured
+        vector_db = apply_provider_embedding_defaults(vector_db, self.config.embedding)
+
         if isinstance(self.config, RemoteMilvusVectorIOConfig):
             consistency_level = self.config.consistency_level
         else: