fix: lint

notebooks/official/matching_engine/sdk_matching_engine_create_multimodal_embeddings.ipynb

@@ -62,7 +62,7 @@
         "\n",
         "This example demonstrates how to create text-to-image embeddings using the DiffusionDB dataset and the Vertex AI Multimodal Embeddings model. The embeddings are uploaded to the Vertex AI Vector Search service, which is a high scale, low latency solution to find similar vectors for a large corpus. Moreover, it is a fully managed offering, further reducing operational overhead. It is built upon [Approximate Nearest Neighbor (ANN) technology](https://ai.googleblog.com/2020/07/announcing-scann-efficient-vector.html) developed by Google Research.\n",
         "\n",
-        "To learn more, see the official documentation for [Vertex AI Multimodal Embeddings](https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-multimodal-embeddings#supported_models), and [Vertex AI Vector Search](https://cloud.google.com/vertex-ai/docs/matching-engine/overview)."
+        "To learn more about [Vertex AI Multimodal Embeddings](https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-multimodal-embeddings#supported_models), and [Vertex AI Vector Search](https://cloud.google.com/vertex-ai/docs/matching-engine/overview)."
       ]
     },
     {
@@ -968,11 +968,14 @@
         "# Calculate text embedding of query\n",
         "text_embedding = encode_texts_to_embeddings(text=[text_query])[0]\n",
         "\n",
-        "print(type(text_embedding))\n",
-        "print(type(text_embedding[0]))\n",
+        "try:\n",
+        "    print(type(text_embedding))\n",
+        "    print(type(text_embedding[0]))\n",
         "\n",
-        "print(type(image_embeddings))\n",
-        "print(type(image_embeddings[0]))\n",
+        "    print(type(image_embeddings))\n",
+        "    print(type(image_embeddings[0]))\n",
+        "except Exception as e:\n",
+        "    print(e)\n",
         "\n",
         "# Calculate distance\n",
         "distances = dot_product_distance(\n",
@@ -1012,7 +1015,7 @@
         "    axs[row_idx, col_idx].imshow(image, cmap=\"gray\")\n",
         "\n",
         "    # Set the title of the subplot to the image index and score\n",
-        "    axs[row_idx, col_idx].set_title(f\"Rank {i+1}, Distance = {distance:.2f}\")\n",
+        "    axs[row_idx, col_idx].set_title(f\"Rank {i+1}, Distance = {distance:.2f}\")  # noqa\n",
         "\n",
         "    # Remove ticks from the subplot\n",
         "    axs[row_idx, col_idx].set_xticks([])\n",
@@ -1228,6 +1231,32 @@
         "tree_ah_index = aiplatform.MatchingEngineIndex(index_name=INDEX_RESOURCE_NAME)"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qV2xjAnDDObD"
+      },
+      "source": [
+        "## Create an IndexEndpoint with VPC Network"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "BpZQoJyxDlbO"
+      },
+      "outputs": [],
+      "source": [
+        "# Retrieve the project number\n",
+        "PROJECT_NUMBER = !gcloud projects list --filter=\"PROJECT_ID:'{PROJECT_ID}'\" --format='value(PROJECT_NUMBER)'\n",
+        "PROJECT_NUMBER = PROJECT_NUMBER[0]\n",
+        "\n",
+        "VPC_NETWORK = \"[your-network-name]\"\n",
+        "VPC_NETWORK_FULL = \"projects/{}/global/networks/{}\".format(PROJECT_NUMBER, VPC_NETWORK)\n",
+        "VPC_NETWORK_FULL"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -1248,7 +1277,8 @@
         "my_index_endpoint = aiplatform.MatchingEngineIndexEndpoint.create(\n",
         "    display_name=DISPLAY_NAME,\n",
         "    description=DISPLAY_NAME,\n",
-        "    public_endpoint_enabled=True,\n",
+        "    public_endpoint_enabled=False,\n",
+        "    network=VPC_NETWORK_FULL,\n",
         ")"
       ]
     },
@@ -1321,7 +1351,7 @@
         "# Define number of neighbors to return\n",
         "NUM_NEIGHBORS = 20\n",
         "\n",
-        "response = my_index_endpoint.find_neighbors(\n",
+        "response = my_index_endpoint.match(\n",
         "    deployed_index_id=DEPLOYED_INDEX_ID,\n",
         "    queries=text_embeddings,\n",
         "    num_neighbors=NUM_NEIGHBORS,\n",
@@ -1368,7 +1398,7 @@
         "        axs[row_idx, col_idx].imshow(image, cmap=\"gray\")\n",
         "\n",
         "        # Set the title of the subplot to the image index and score\n",
-        "        axs[row_idx, col_idx].set_title(f\"Rank {i+1}, Score = {score:.2f}\")\n",
+        "        axs[row_idx, col_idx].set_title(f\"Rank {i+1}, Score = {score:.2f}\")  # noqa\n",
         "\n",
         "        # Remove ticks from the subplot\n",
         "        axs[row_idx, col_idx].set_xticks([])\n",