diff --git a/examples/image_search/example.ipynb b/examples/image_search/example.ipynb
index ddca260..7c7ba74 100644
--- a/examples/image_search/example.ipynb
+++ b/examples/image_search/example.ipynb
@@ -89,11 +89,7 @@
     "\n",
     "    id = Column(Integer, primary_key=True)\n",
     "    image_id = Column(Integer)\n",
-    "    embedding = Column(\n",
-    "        VectorType(CLIP_DIMENSION),\n",
-    "        # using hnsw index with cosine distance\n",
-    "        comment=\"hnsw(distance=cosine)\"\n",
-    "    )\n",
+    "    embedding = Column(VectorType(CLIP_DIMENSION))\n",
     "\n",
     "Base.metadata.drop_all(engine)\n",
     "Base.metadata.create_all(engine)"
diff --git a/examples/jina-ai-embeddings-demo/jina-ai-embeddings-demo.py b/examples/jina-ai-embeddings-demo/jina-ai-embeddings-demo.py
index 987f3da..44e7ae9 100644
--- a/examples/jina-ai-embeddings-demo/jina-ai-embeddings-demo.py
+++ b/examples/jina-ai-embeddings-demo/jina-ai-embeddings-demo.py
@@ -47,7 +47,6 @@ class Document(Base):
         # DIMENSIONS is determined by the embedding model,
         # for Jina AI's jina-embeddings-v2-base-en model it's 768.
         VectorType(dim=768),
-        comment="hnsw(distance=cosine)"
     )
 
 
diff --git a/examples/orm-peewee-quickstart/.env.example b/examples/orm-peewee-quickstart/.env.example
index e5bd2ae..a1d8448 100644
--- a/examples/orm-peewee-quickstart/.env.example
+++ b/examples/orm-peewee-quickstart/.env.example
@@ -3,5 +3,6 @@ TIDB_PORT=4000
 TIDB_USERNAME=******.root
 TIDB_PASSWORD=********
 TIDB_DATABASE=test
-# For macOS. For other platforms, please refer https://docs.pingcap.com/tidbcloud/secure-connections-to-serverless-clusters#root-certificate-default-path.
-TIDB_CA_PATH=/etc/ssl/cert.pem
\ No newline at end of file
+# TiDB Serverless Cluster requires SSL connection for public network access.
+# For local TiDB cluster, please set TIDB_SSL=false to disable SSL.
+TIDB_SSL=true
\ No newline at end of file
diff --git a/examples/orm-peewee-quickstart/peewee-quickstart.py b/examples/orm-peewee-quickstart/peewee-quickstart.py
index a46b47f..5e77613 100644
--- a/examples/orm-peewee-quickstart/peewee-quickstart.py
+++ b/examples/orm-peewee-quickstart/peewee-quickstart.py
@@ -1,21 +1,22 @@
 import os
 import dotenv
 
-from peewee import Model, MySQLDatabase, SQL, TextField
-from tidb_vector.peewee import VectorField
+from tidb_vector.peewee import VectorField, VectorAdaptor
+from tidb_vector.constants import DistanceMetric
+from peewee import Model, MySQLDatabase, TextField
 
 dotenv.load_dotenv()
 
 # Step 1: Connect to TiDB using Peewee.
 
 # Using `pymysql` as the driver.
-connect_kwargs = {
+ssl_kwargs = {
     'ssl_verify_cert': True,
     'ssl_verify_identity': True,
 }
 
 # Using `mysqlclient` as the driver.
-# connect_kwargs = {
+# ssl_kwargs = {
 #     'ssl_mode': 'VERIFY_IDENTITY',
 #     'ssl': {
 #         # Root certificate default path
@@ -30,7 +31,7 @@
     password=os.environ.get('TIDB_PASSWORD', ''),
     host=os.environ.get('TIDB_HOST', 'localhost'),
     port=int(os.environ.get('TIDB_PORT', '4000')),
-    **connect_kwargs,
+    **ssl_kwargs if os.environ.get('TIDB_SSL', 'false').lower() == 'true' else {},
 )
 
 
@@ -53,12 +54,16 @@ class Meta:
         table_name = 'peewee_demo_documents_with_index'
 
     content = TextField()
-    embedding = VectorField(3, constraints=[SQL("COMMENT 'hnsw(distance=cosine)'")])
+    embedding = VectorField(3)
 
 
 db.connect()
 db.drop_tables([Document, DocumentWithIndex])
 db.create_tables([Document, DocumentWithIndex])
+VectorAdaptor(db).create_vector_index(
+    DocumentWithIndex.embedding,
+    DistanceMetric.COSINE,
+)
 
 # Step 3. Insert embeddings into the table.
 Document.create(content='dog', embedding=[1, 2, 1])
diff --git a/examples/orm-sqlalchemy-quickstart/.env.example b/examples/orm-sqlalchemy-quickstart/.env.example
index 5c55f22..a1d8448 100644
--- a/examples/orm-sqlalchemy-quickstart/.env.example
+++ b/examples/orm-sqlalchemy-quickstart/.env.example
@@ -1 +1,8 @@
-TIDB_DATABASE_URL=mysql+pymysql://<USERNAME>:<PASSWORD>@<HOST>:4000/<DATABASE>?ssl_ca=<CA>&ssl_verify_cert=true&ssl_verify_identity=true
\ No newline at end of file
+TIDB_HOST=gateway01.****.prod.aws.tidbcloud.com
+TIDB_PORT=4000
+TIDB_USERNAME=******.root
+TIDB_PASSWORD=********
+TIDB_DATABASE=test
+# TiDB Serverless Cluster requires SSL connection for public network access.
+# For local TiDB cluster, please set TIDB_SSL=false to disable SSL.
+TIDB_SSL=true
\ No newline at end of file
diff --git a/examples/orm-sqlalchemy-quickstart/sqlalchemy-quickstart.py b/examples/orm-sqlalchemy-quickstart/sqlalchemy-quickstart.py
index 1883a59..ec13061 100644
--- a/examples/orm-sqlalchemy-quickstart/sqlalchemy-quickstart.py
+++ b/examples/orm-sqlalchemy-quickstart/sqlalchemy-quickstart.py
@@ -1,15 +1,43 @@
 import os
 import dotenv
 
-from sqlalchemy import Column, Integer, create_engine, Text
+from sqlalchemy import Column, Integer, create_engine, Text, URL
 from sqlalchemy.orm import declarative_base, Session
-from tidb_vector.sqlalchemy import VectorType
+from tidb_vector.sqlalchemy import VectorType, VectorAdaptor
+from tidb_vector.constants import DistanceMetric
 
 dotenv.load_dotenv()
 
 # Step 1: Connect to TiDB using SQLAlchemy.
-tidb_connection_string = os.environ['TIDB_DATABASE_URL']
-engine = create_engine(tidb_connection_string)
+
+# Using `pymysql` as the driver.
+drivername = 'mysql+pymysql'
+ssl_kwargs = {
+    'ssl_verify_cert': 'true',
+    'ssl_verify_identity': 'true',
+}
+
+# Using `mysqlclient` as the driver.
+# drivername = 'mysql+mysqldb'
+# ssl_kwargs = {
+#     'ssl_mode': 'VERIFY_IDENTITY',
+#     'ssl': {
+#         # Root certificate default path
+#         # https://docs.pingcap.com/tidbcloud/secure-connections-to-serverless-clusters/#root-certificate-default-path
+#         'ca': os.environ.get('TIDB_CA_PATH', '/path/to/ca.pem'),
+#     },
+# }
+
+engine = create_engine(URL.create(
+    drivername=drivername,
+    username=os.environ['TIDB_USERNAME'],
+    password=os.environ['TIDB_PASSWORD'],
+    host=os.environ['TIDB_HOST'],
+    port=os.environ['TIDB_PORT'],
+    database=os.environ['TIDB_DATABASE'],
+    query=ssl_kwargs if os.environ.get('TIDB_SSL', 'false').lower() == 'true' else {},
+))
+
 
 # Step 2: Define a table with a vector column.
 Base = declarative_base()
@@ -27,11 +55,16 @@ class DocumentWithIndex(Base):
     __tablename__ = 'sqlalchemy_demo_documents_with_index'
     id = Column(Integer, primary_key=True)
     content = Column(Text)
-    embedding = Column(VectorType(3), comment="hnsw(distance=cosine)")
+    embedding = Column(VectorType(3))
 
 
 Base.metadata.drop_all(engine)
 Base.metadata.create_all(engine)
+VectorAdaptor(engine).create_vector_index(
+    DocumentWithIndex.embedding,
+    DistanceMetric.COSINE,
+    skip_existing=True,
+)
 
 
 # Step 3: Insert embeddings into the table.
diff --git a/examples/semantic-cache/cache.py b/examples/semantic-cache/cache.py
index 413d28e..6935041 100644
--- a/examples/semantic-cache/cache.py
+++ b/examples/semantic-cache/cache.py
@@ -57,7 +57,6 @@ class Cache(SQLModel, table=True):
         sa_column=Column(
             VectorType(768),
             default=None,
-            comment="hnsw(distance=l2)",
             nullable=False,
         )
     )