feat: configure vector-io provider with an embedding model

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

llama_stack/providers/utils/memory/vector_store.py

@@ -31,6 +31,8 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
     interleaved_content_as_str,
 )
 from llama_stack.providers.utils.vector_io.vector_utils import generate_chunk_id
+from llama_stack.providers.utils.vector_io.chunk_utils import generate_chunk_id
+from llama_stack.providers.utils.vector_io.embedding_config import EmbeddingConfig
 
 log = logging.getLogger(__name__)
 
@@ -39,6 +41,41 @@ RERANKER_TYPE_RRF = "rrf"
 RERANKER_TYPE_WEIGHTED = "weighted"
 
 
+def apply_provider_embedding_defaults(
+    vector_db: VectorDB, provider_embedding_config: EmbeddingConfig | None
+) -> VectorDB:
+    """Apply provider-level embedding defaults to a VectorDB if not already specified.
+
+    This allows providers to specify default embedding models for use-case specific
+    vector stores, reducing the need for app developers to know embedding details.
+
+    Args:
+        vector_db: The VectorDB to potentially modify
+        provider_embedding_config: The provider's default embedding configuration
+
+    Returns:
+        The VectorDB with embedding defaults applied if needed
+    """
+    if provider_embedding_config is None:
+        return vector_db
+
+    # Create a copy to avoid modifying the original
+    db_dict = vector_db.model_dump()
+
+    # Apply embedding model default if not specified
+    if not db_dict.get("embedding_model") and provider_embedding_config.model:
+        db_dict["embedding_model"] = provider_embedding_config.model
+
+    # Apply embedding dimension default if not specified
+    if not db_dict.get("embedding_dimension") and provider_embedding_config.dimensions:
+        db_dict["embedding_dimension"] = provider_embedding_config.dimensions
+    elif not db_dict.get("embedding_dimension"):
+        # Fallback to default dimension if still not specified
+        db_dict["embedding_dimension"] = provider_embedding_config.get_dimensions_or_default()
+
+    return VectorDB.model_validate(db_dict)
+
+
 def parse_pdf(data: bytes) -> str:
     # For PDF and DOC/DOCX files, we can't reliably convert to string
     pdf_bytes = io.BytesIO(data)