feat: configure vector-io provider with an embedding model

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

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

@@ -6,12 +6,13 @@
 
 from typing import Any
 
-from pydantic import BaseModel
+from pydantic import BaseModel, 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
 
 
@@ -28,6 +29,10 @@ class QdrantVectorIOConfig(BaseModel):
     timeout: int | None = None
     host: str | None = None
     kvstore: KVStoreConfig
+    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.",
+    )
 
     @classmethod
     def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> dict[str, Any]:
@@ -37,4 +42,10 @@ class QdrantVectorIOConfig(BaseModel):
                 __distro_dir__=__distro_dir__,
                 db_name="qdrant_registry.db",
             ),
+            "api_key": "${env.QDRANT_API_KEY}",
+            # Optional: Configure default embedding model for this provider
+            # "embedding": {
+            #     "model": "${env.QDRANT_EMBEDDING_MODEL:=all-MiniLM-L6-v2}",
+            #     "dimensions": 384
+            # },
         }