docs: improve vector store config documentation and fix test isolation

docs/source/distributions/configuration.md

@@ -688,13 +688,26 @@ shields:
 ...
 ```
 
-### Global Vector-Store Defaults
+## Global Vector Store Defaults
 
-You can provide a *stack-level* default embedding model that will be used whenever a new vector-store is created and the caller does **not** specify an `embedding_model` parameter.
+You can provide a stack-level default embedding model that will be used whenever a new vector store is created and the caller does not specify an `embedding_model` parameter.
 
-Add a top-level block next to `models:` and `vector_io:` in your build/run YAML:
+Add a top-level `vector_store_config` block at the root of your build/run YAML, alongside other root-level keys such as `models`, `shields`, `server`, and `metadata_store`:
 
 ```yaml
+# ... other configuration sections ...
+metadata_store:
+  namespace: null
+  type: sqlite
+  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ollama}/registry.db
+models:
+- metadata: {}
+  model_id: ${env.INFERENCE_MODEL}
+  provider_id: ollama
+  provider_model_id: null
+shields: []
+server:
+  port: 8321
 vector_store_config:
   default_embedding_model: ${env.LLAMA_STACK_DEFAULT_EMBEDDING_MODEL:=all-MiniLM-L6-v2}
   # optional - if omitted, defaults to 384

tests/unit/common/test_vector_store_config.py

@@ -7,7 +7,10 @@
 from llama_stack.apis.common.vector_store_config import VectorStoreConfig
 
 
-def test_defaults():
+def test_defaults(monkeypatch):
+    # ensure env is clean to avoid flaky defaults
+    monkeypatch.delenv("LLAMA_STACK_DEFAULT_EMBEDDING_MODEL", raising=False)
+    monkeypatch.delenv("LLAMA_STACK_DEFAULT_EMBEDDING_DIMENSION", raising=False)
     config = VectorStoreConfig()
     assert config.default_embedding_model is None
     assert config.default_embedding_dimension is None

tests/unit/router/test_embedding_precedence.py

@@ -5,9 +5,13 @@
 # the root directory of this source tree.
 
 
+import pytest
+
 from llama_stack.apis.models import ModelType
 from llama_stack.core.routers.vector_io import VectorIORouter
 
+pytestmark = pytest.mark.asyncio
+
 
 class _DummyModel:
     def __init__(self, identifier: str, dim: int):