@@ -53,10 +53,10 @@
 from feast.errors import FeastProviderLoginError
 from feast.repo_config import load_repo_config
 from feast.repo_operations import (
+    DEFAULT_DEMO_REPOS,
     apply_total,
     cli_check_repo,
     create_feature_store,
-    DEFAULT_DEMO_REPOS,
     generate_project_name,
     init_repo,
     plan,
@@ -424,6 +424,7 @@ def materialize_incremental_command(ctx: click.Context, end_ts: str, views: List
         end_date=utils.make_tzaware(datetime.fromisoformat(end_ts)),
     )
 
+
 @cli.command("init")
 @click.argument("PROJECT_DIRECTORY", required=False)
 @click.option(
@@ -481,7 +482,12 @@ def init_command(
         )
         base_path.mkdir(parents=True, exist_ok=True)
         for repo_name, template_name in DEFAULT_DEMO_REPOS:
-            init_repo(repo_name, template_name, str(base_path / repo_name))
+            init_repo(
+                repo_name,
+                template_name,
+                str(base_path / repo_name),
+                shared_registry=True,
+            )
         return
 
     init_repo(project_directory, template, repo_path)
 
@@ -488,9 +488,17 @@ def cli_check_repo(repo_path: Path, fs_yaml_file: Path):
         )
         sys.exit(1)
 
+
 DEFAULT_DEMO_REPOS = [("driver", "local"), ("rag", "rag")]
 
-def init_repo(repo_name: str, template: str, repo_path: Optional[str] = None):
+
+def init_repo(
+    repo_name: str,
+    template: str,
+    repo_path: Optional[str] = None,
+    *,
+    shared_registry: bool = False,
+):
     import os
     from pathlib import Path
     from shutil import copytree
@@ -555,8 +563,12 @@ def init_repo(repo_name: str, template: str, repo_path: Optional[str] = None):
         feature_store_yaml_path, "project: my_project", f"project: {repo_name}"
     )
 
-    # Default driver+rag demos: shared registry at project root.
-    if (repo_name, template) in DEFAULT_DEMO_REPOS and feature_store_yaml_path.exists():
+    # Multi-demo default flow only: shared registry at project root.
+    if (
+        shared_registry
+        and (repo_name, template) in DEFAULT_DEMO_REPOS
+        and feature_store_yaml_path.exists()
+    ):
         with open(feature_store_yaml_path) as f:
             config = yaml.safe_load(f)
         if config and isinstance(config.get("registry"), str):
 
@@ -1,4 +1,3 @@
-
 def bootstrap():
     # Bootstrap() is called from init_repo() during `feast init`
     import pathlib
@@ -15,22 +14,39 @@ def bootstrap():
     embedding_dim = 384
     now = datetime.now().replace(microsecond=0, tzinfo=None)
     cities = [
-        (1, "New York", "New York", "New York City is the most populous city in the United States."),
-        (2, "Los Angeles", "California", "Los Angeles is the second most populous city in the United States."),
-        (3, "Chicago", "Illinois", "Chicago is the third most populous city in the United States."),
+        (
+            1,
+            "New York",
+            "New York",
+            "New York City is the most populous city in the United States.",
+        ),
+        (
+            2,
+            "Los Angeles",
+            "California",
+            "Los Angeles is the second most populous city in the United States.",
+        ),
+        (
+            3,
+            "Chicago",
+            "Illinois",
+            "Chicago is the third most populous city in the United States.",
+        ),
     ]
     rows = []
     for city_id, city_name, state, wiki_summary in cities:
         vec = np.random.randn(embedding_dim).astype(np.float32)
         vec = (vec / np.linalg.norm(vec)).tolist()
-        rows.append({
-            "city_id": city_id,
-            "event_timestamp": pd.Timestamp(now),
-            "vector": vec,
-            "sentence_chunks": wiki_summary[:200],
-            "state": f"{city_name}, {state}",
-            "wiki_summary": wiki_summary,
-        })
+        rows.append(
+            {
+                "city_id": city_id,
+                "event_timestamp": pd.Timestamp(now),
+                "vector": vec,
+                "sentence_chunks": wiki_summary[:200],
+                "state": f"{city_name}, {state}",
+                "wiki_summary": wiki_summary,
+            }
+        )
     df = pd.DataFrame(rows)
     parquet_path = data_path / "city_wikipedia_summaries_with_embeddings.parquet"
     df.to_parquet(path=str(parquet_path), index=False)