feat: configure vector-io provider with an embedding model

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

llama_stack/providers/utils/vector_io/embedding_config.py

@@ -0,0 +1,36 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from pydantic import BaseModel, Field
+
+from llama_stack.schema_utils import json_schema_type
+
+
+@json_schema_type
+class EmbeddingConfig(BaseModel):
+    """Configuration for embedding model used by vector-io providers.
+
+    This allows providers to specify default embedding models for use-case specific
+    vector stores, reducing the need for app developers to know embedding details.
+
+    Example usage in provider config:
+    ```yaml
+    vector_io:
+      - provider_id: question-answer
+        provider_type: remote::pgvector
+        config:
+          embedding:
+            model: prod/question-answer-embedder
+            dimensions: 384
+    ```
+    """
+
+    model: str = Field(description="The embedding model identifier to use")
+    dimensions: int | None = Field(default=None, description="The embedding dimensions (optional, can be inferred)")
+
+    def get_dimensions_or_default(self, default: int = 384) -> int:
+        """Get dimensions with fallback to default if not specified."""
+        return self.dimensions if self.dimensions is not None else default