diff --git a/examples/amazon_s3_embedding/README.md b/examples/amazon_s3_embedding/README.md
index 66bf5fa3..3273716d 100644
--- a/examples/amazon_s3_embedding/README.md
+++ b/examples/amazon_s3_embedding/README.md
@@ -37,12 +37,6 @@ Install dependencies:
 pip install -e .
 ```
 
-Setup:
-
-```sh
-cocoindex setup main.py
-```
-
 Run:
 
 ```sh
diff --git a/examples/amazon_s3_embedding/main.py b/examples/amazon_s3_embedding/main.py
index b3740583..fd23654d 100644
--- a/examples/amazon_s3_embedding/main.py
+++ b/examples/amazon_s3_embedding/main.py
@@ -2,6 +2,7 @@
 from psycopg_pool import ConnectionPool
 import cocoindex
 import os
+from typing import Any
 
 
 @cocoindex.transform_flow()
@@ -22,7 +23,7 @@ def text_to_embedding(
 @cocoindex.flow_def(name="AmazonS3TextEmbedding")
 def amazon_s3_text_embedding_flow(
     flow_builder: cocoindex.FlowBuilder, data_scope: cocoindex.DataScope
-):
+) -> None:
     """
     Define an example flow that embeds text from Amazon S3 into a vector database.
     """
@@ -72,7 +73,7 @@ def amazon_s3_text_embedding_flow(
     )
 
 
-def search(pool: ConnectionPool, query: str, top_k: int = 5):
+def search(pool: ConnectionPool, query: str, top_k: int = 5) -> list[dict[str, Any]]:
     # Get the table name, for the export target in the amazon_s3_text_embedding_flow above.
     table_name = cocoindex.utils.get_target_default_name(
         amazon_s3_text_embedding_flow, "doc_embeddings"
@@ -95,9 +96,11 @@ def search(pool: ConnectionPool, query: str, top_k: int = 5):
             ]
 
 
-def _main():
+def _main() -> None:
     # Initialize the database connection pool.
     pool = ConnectionPool(os.getenv("COCOINDEX_DATABASE_URL"))
+
+    amazon_s3_text_embedding_flow.setup()
     with cocoindex.FlowLiveUpdater(amazon_s3_text_embedding_flow):
         # Run queries in a loop to demonstrate the query capabilities.
         while True:
diff --git a/examples/amazon_s3_embedding/pyproject.toml b/examples/amazon_s3_embedding/pyproject.toml
index c005d4e0..2fa97919 100644
--- a/examples/amazon_s3_embedding/pyproject.toml
+++ b/examples/amazon_s3_embedding/pyproject.toml
@@ -3,7 +3,7 @@ name = "amazon-s3-text-embedding"
 version = "0.1.0"
 description = "Simple example for cocoindex: build embedding index based on Amazon S3 files."
 requires-python = ">=3.11"
-dependencies = ["cocoindex[embeddings]>=0.1.52", "python-dotenv>=1.0.1"]
+dependencies = ["cocoindex[embeddings]>=0.1.57", "python-dotenv>=1.0.1"]
 
 [tool.setuptools]
 packages = []
diff --git a/examples/image_search/README.md b/examples/image_search/README.md
index 017fc1fc..749c0bac 100644
--- a/examples/image_search/README.md
+++ b/examples/image_search/README.md
@@ -29,7 +29,6 @@ We appreciate a star ⭐ at [CocoIndex Github](https://github.com/cocoindex-io/c
 
 - Run Backend
   ```
-  cocoindex setup main.py
   uvicorn main:app --reload --host 0.0.0.0 --port 8000
   ```
 
diff --git a/examples/image_search/main.py b/examples/image_search/main.py
index 629acad2..2ce30b9d 100644
--- a/examples/image_search/main.py
+++ b/examples/image_search/main.py
@@ -84,9 +84,11 @@ def image_object_embedding_flow(
 
 
 @asynccontextmanager
-async def lifespan(app: FastAPI):
+async def lifespan(app: FastAPI) -> None:
     load_dotenv()
     cocoindex.init()
+    image_object_embedding_flow.setup(report_to_stdout=True)
+
     app.state.qdrant_client = QdrantClient(url=QDRANT_URL, prefer_grpc=True)
 
     # Start updater
diff --git a/examples/image_search/pyproject.toml b/examples/image_search/pyproject.toml
index f24a1189..d4bfc3a9 100644
--- a/examples/image_search/pyproject.toml
+++ b/examples/image_search/pyproject.toml
@@ -4,7 +4,7 @@ version = "0.1.0"
 description = "Simple example for cocoindex: build embedding index based on images."
 requires-python = ">=3.11"
 dependencies = [
-    "cocoindex>=0.1.52",
+    "cocoindex>=0.1.57",
     "python-dotenv>=1.0.1",
     "fastapi>=0.100.0",
     "torch>=2.0.0",