diff --git a/alloydb/notebooks/batch_embeddings_update.ipynb b/alloydb/notebooks/batch_embeddings_update.ipynb
index 4c836eb88b17..5a1ac3e7d0b0 100644
--- a/alloydb/notebooks/batch_embeddings_update.ipynb
+++ b/alloydb/notebooks/batch_embeddings_update.ipynb
@@ -55,22 +55,6 @@
         "* A Google Cloud Account and Google Cloud Project"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "vHdR4fF3vLWA"
-      },
-      "source": [
-        "## Objectives\n",
-        "\n",
-        "In the following instructions you will learn to:\n",
-        "\n",
-        "1. Install required dependencies for our application\n",
-        "2. Set up authentication for our project\n",
-        "3. Set up a AlloyDB for PostgreSQL Instance\n",
-        "4. Import the data used by our application"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -382,6 +366,17 @@
         "    return pool"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from google.cloud.alloydb.connector import AsyncConnector\n",
+        "\n",
+        "connector = AsyncConnector()"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -405,15 +400,9 @@
       },
       "outputs": [],
       "source": [
-        "from sqlalchemy.ext.asyncio import AsyncEngine, create_async_engine\n",
         "from sqlalchemy import text, exc\n",
         "\n",
-        "from google.cloud.alloydb.connector import AsyncConnector, IPTypes\n",
-        "\n",
-        "async def create_db(database_name):\n",
-        "    # Get a raw connection directly from the connector\n",
-        "    connector = AsyncConnector()\n",
-        "    connection_string = f\"projects/{project_id}/locations/{region}/clusters/{cluster_name}/instances/{instance_name}\"\n",
+        "async def create_db(database_name, connector):    \n",
         "    pool = await init_connection_pool(connector, \"postgres\")\n",
         "    async with pool.connect() as conn:\n",
         "        try:\n",
@@ -423,7 +412,7 @@
         "        except exc.ProgrammingError:\n",
         "          print(f\"Database '{database_name}' already exists\")\n",
         "\n",
-        "await create_db(database_name=database_name)"
+        "await create_db(database_name=database_name, connector=connector)"
       ]
     },
     {
@@ -600,7 +589,7 @@
         "        \"overview\": row[\"overview\"],\n",
         "        \"analysis\": row[\"analysis\"],\n",
         "    }\n",
-        "    for index, row in df.iterrows()\n",
+        "    for _, row in df.iterrows()\n",
         "]"
       ]
     },
@@ -614,8 +603,6 @@
       "source": [
         "from google.cloud.alloydb.connector import AsyncConnector\n",
         "\n",
-        "connector = AsyncConnector()\n",
-        "\n",
         "# Create table and insert data\n",
         "async def insert_data(pool):\n",
         "  async with pool.connect() as db_conn:\n",
@@ -1042,7 +1029,6 @@
       "source": [
         "import vertexai\n",
         "import time\n",
-        "import asyncio\n",
         "from vertexai.language_models import TextEmbeddingModel\n",
         "\n",
         "pool_size = 10\n",
@@ -1051,7 +1037,6 @@
         "total_char_count = 0\n",
         "\n",
         "# Set up connections to the database\n",
-        "connector = AsyncConnector()\n",
         "pool = await init_connection_pool(connector, database_name, pool_size=pool_size)\n",
         "\n",
         "# Initialise VertexAI and the model to be used to generate embeddings\n",
@@ -1067,10 +1052,14 @@
         "batch_data = batch_source_data(source_data, cols_to_embed)\n",
         "\n",
         "# Generate embeddings for the batched data concurrently\n",
-        "embeddings_data = embed_objects_concurrently(cols_to_embed, batch_data, model, task, max_concurrency=embed_data_concurrency)\n",
+        "embeddings_data = embed_objects_concurrently(\n",
+        "    cols_to_embed, batch_data, model, task, max_concurrency=embed_data_concurrency\n",
+        ")\n",
         "\n",
         "# Update the database with the generated embeddings concurrently\n",
-        "await batch_update_rows_concurrently(pool, embeddings_data, cols_to_embed, max_concurrency=batch_update_concurrency)\n",
+        "await batch_update_rows_concurrently(\n",
+        "    pool, embeddings_data, cols_to_embed, max_concurrency=batch_update_concurrency\n",
+        ")\n",
         "\n",
         "end_time = time.monotonic()\n",
         "elapsed_time = end_time - start_time\n",
@@ -1084,15 +1073,6 @@
         "print(f\"Total run time: {elapsed_time:.2f} seconds\")\n",
         "print(f\"Total characters embedded: {total_char_count}\")"
       ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 41,
-      "metadata": {
-        "id": "fzZJsWRZAMxs"
-      },
-      "outputs": [],
-      "source": []
     }
   ],
   "metadata": {