Merge branch 'main' into imkc--matching-engine-notebook-fix

notebooks/community/matching_engine/sdk_matching_engine_for_indexing.ipynb

@@ -98,10 +98,10 @@
         "## Before you begin\n",
         "\n",
         "* **Prepare a VPC network**.  To reduce any network overhead that might lead to unnecessary increase in overhead latency, it is best to call the ANN endpoints from your VPC via a direct [VPC Peering](https://cloud.google.com/vertex-ai/docs/general/vpc-peering) connection. The following section describes how to setup a VPC Peering connection if you don't have one. This is a one-time initial setup task. You can also reuse existing VPC network and skip this section.\n",
-        "* **WARNING:** The match service gRPC API (to create online queries against your deployed index) has to be executed in a Google Cloud Notebook instance that is created with the following requirements:\n",
+        "* **WARNING:** The MatchingIndexEndpoint.match method (to create online queries against your deployed index) has to be executed in a Vertex AI Workbench notebook instance that is created with the following requirements:\n",
         "  * **In the same region as where your ANN service is deployed** (for example, if you set `REGION = \"us-central1\"` as same as the tutorial, the notebook instance has to be in `us-central1`).\n",
         "  * **Make sure you select the VPC network you created for ANN service** (instead of using the \"default\" one). That is, you will have to create the VPC network below and then create a new notebook instance that uses that VPC.  \n",
-        "  * If you run it in the colab or a Google Cloud Notebook instance in a different VPC network or region, the gRPC API will fail to peer the network (InactiveRPCError)."
+        "  * If you run it in the colab or a Vertex AI Workbench notebook instance in a different VPC network or region, the gRPC API will fail to peer the network (InactiveRPCError)."
       ]
     },
     {
@@ -114,9 +114,9 @@
       "source": [
         "PROJECT_ID = \"<your_project_id>\"  # @param {type:\"string\"}\n",
         "\n",
-        "NETWORK_NAME = \"ucaip-haystack-vpc-network\"  # @param {type:\"string\"}\n",
+        "NETWORK_NAME = \"my-vpc-network\"  # @param {type:\"string\"}\n",
         "\n",
-        "PEERING_RANGE_NAME = \"ucaip-haystack-range\""
+        "PEERING_RANGE_NAME = \"my-haystack-range\""
       ]
     },
     {
@@ -140,7 +140,7 @@
         "! gcloud compute firewall-rules create {NETWORK_NAME}-allow-ssh --network {NETWORK_NAME} --priority 65534 --project {PROJECT_ID} --allow tcp:22\n",
         "\n",
         "# Reserve IP range\n",
-        "! gcloud compute addresses create {PEERING_RANGE_NAME} --global --prefix-length=16 --network={NETWORK_NAME} --purpose=VPC_PEERING --project={PROJECT_ID} --description=\"peering range for uCAIP Haystack.\"\n",
+        "! gcloud compute addresses create {PEERING_RANGE_NAME} --global --prefix-length=16 --network={NETWORK_NAME} --purpose=VPC_PEERING --project={PROJECT_ID} --description=\"peering range\"\n",
         "\n",
         "# Set up peering with service networking\n",
         "! gcloud services vpc-peerings connect --service=servicenetworking.googleapis.com --network={NETWORK_NAME} --ranges={PEERING_RANGE_NAME} --project={PROJECT_ID}"
@@ -152,7 +152,7 @@
         "id": "d3uj8x73nDX_"
       },
       "source": [
-        "* Authentication: `$ gcloud auth login` rerun this in Google Cloud Notebook terminal when you are logged out and need the credential again."
+        "* Authentication: Rerun the `gcloud auth login` command in the Vertex AI Workbench notebook terminal when you are logged out and need the credential again."
       ]
     },
     {
@@ -163,7 +163,7 @@
       "source": [
         "### Installation\n",
         "\n",
-        "Download and install the latest (preview) version of the Vertex SDK for Python."
+        "Download and install the latest version of the Vertex SDK for Python."
       ]
     },
     {
@@ -174,7 +174,7 @@
       },
       "outputs": [],
       "source": [
-        "! pip install -U git+https://github.com/ivanmkc/python-aiplatform.git@imkc--matching-engine"
+        "! pip install -U google-cloud-aiplatform"
       ]
     },
     {
@@ -333,7 +333,7 @@
       "source": [
         "### Authenticate your Google Cloud account\n",
         "\n",
-        "**If you are using Google Cloud Notebooks**, your environment is already\n",
+        "**If you are using a Vertex AI Workbench notebook**, your environment is already\n",
         "authenticated. Skip this step."
       ]
     },
@@ -383,11 +383,13 @@
         "# Cloud Storage bucket and lets you submit training jobs and prediction\n",
         "# requests.\n",
         "\n",
-        "# The Google Cloud Notebook product has specific requirements\n",
-        "IS_GOOGLE_CLOUD_NOTEBOOK = os.path.exists(\"/opt/deeplearning/metadata/env_version\")\n",
+        "# The Vertex AI Workbench notebook product has specific requirements\n",
+        "IS_VERTEX_AI_WORKBENCH_NOTEBOOK = os.path.exists(\n",
+        "    \"/opt/deeplearning/metadata/env_version\"\n",
+        ")\n",
         "\n",
-        "# If on Google Cloud Notebooks, then don't execute this code\n",
-        "if not IS_GOOGLE_CLOUD_NOTEBOOK:\n",
+        "# If on a Vertex AI Workbench notebook, then don't execute this code\n",
+        "if not IS_VERTEX_AI_WORKBENCH_NOTEBOOK:\n",
         "    if \"google.colab\" in sys.modules:\n",
         "        from google.colab import auth as google_auth\n",
         "\n",
@@ -539,12 +541,12 @@
         "id": "lR6Wwv-hCCN-"
       },
       "source": [
-        "## Prepare the Data\n",
+        "## Prepare the data\n",
         "\n",
         "The GloVe dataset consists of a set of pre-trained embeddings. The embeddings are split into a \"train\" split, and a \"test\" split.\n",
         "We will create a vector search index from the \"train\" split, and use the embedding vectors in the \"test\" split as query vectors to test the vector search index.\n",
         "\n",
-        "NOTE: While the data split uses the term \"train\", these are pre-trained embeddings and thus are ready to be indexed for search. The terms \"train\" and \"test\" split are used just to be consistent with usual machine learning terminology.\n",
+        "**Note:** While the data split uses the term \"train\", these are pre-trained embeddings and therefore are ready to be indexed for search. The terms \"train\" and \"test\" split are used just to be consistent with machine learning terminology.\n",
         "\n",
         "Download the GloVe dataset.\n"
       ]