vertex_ai: add google_vertex_ai_index_endpoint_deployed_index resource (#11039) (#764)

[upstream:818c46b4491a3476fa383e5c75fe1dfd2bb304bc]

Signed-off-by: Modular Magician <[email protected]>

Author: modular-magician

vertex_ai_index_endpoint_deployed_index_automatic_resources/backing_file.tf

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+# This file has some scaffolding to make sure that names are unique and that
+# a region and zone are selected when you try to create your Terraform resources.
+
+locals {
+  name_suffix = "${random_pet.suffix.id}"
+}
+
+resource "random_pet" "suffix" {
+  length = 2
+}
+
+provider "google" {
+  region = "us-central1"
+  zone   = "us-central1-c"
+}

vertex_ai_index_endpoint_deployed_index_automatic_resources/main.tf

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+resource "google_vertex_ai_index_endpoint_deployed_index" "automatic_resources" {
+  depends_on = [ google_vertex_ai_index_endpoint.vertex_endpoint ]
+  index_endpoint = google_vertex_ai_index_endpoint.vertex_endpoint.id
+  index = google_vertex_ai_index.index.id // this is the index that will be deployed onto an endpoint
+  deployed_index_id = "deployed_index_id-${local.name_suffix}"
+  display_name = "vertex-deployed-index-${local.name_suffix}"
+  automatic_resources{
+    max_replica_count = 2
+    min_replica_count = 1
+  }
+  deployment_group = "test"
+}
+
+resource "google_storage_bucket" "bucket" {
+  name     = "bucket-name-${local.name_suffix}"
+  location = "us-central1"
+  uniform_bucket_level_access = true
+}
+
+# The sample data comes from the following link:
+# https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#specify-namespaces-tokens
+resource "google_storage_bucket_object" "data" {
+  name   = "contents/data.json"
+  bucket = google_storage_bucket.bucket.name
+  content = <<EOF
+{"id": "42", "embedding": [0.5, 1.0], "restricts": [{"namespace": "class", "allow": ["cat", "pet"]},{"namespace": "category", "allow": ["feline"]}]}
+{"id": "43", "embedding": [0.6, 1.0], "restricts": [{"namespace": "class", "allow": ["dog", "pet"]},{"namespace": "category", "allow": ["canine"]}]}
+EOF
+}
+
+resource "google_vertex_ai_index" "index" {
+  labels = {
+    foo = "bar"
+  }
+  region   = "us-central1"
+  display_name = "test-index-${local.name_suffix}"
+  description = "index for test"
+  metadata {
+    contents_delta_uri = "gs://${google_storage_bucket.bucket.name}/contents"
+    config {
+      dimensions = 2
+      approximate_neighbors_count = 150
+      shard_size = "SHARD_SIZE_MEDIUM"
+      distance_measure_type = "DOT_PRODUCT_DISTANCE"
+      algorithm_config {
+        tree_ah_config {
+          leaf_node_embedding_count = 500
+          leaf_nodes_to_search_percent = 7
+        }
+      }
+    }
+  }
+  index_update_method = "BATCH_UPDATE"
+}
+
+
+resource "google_vertex_ai_index_endpoint" "vertex_endpoint" {
+  display_name = "sample-endpoint"
+  description  = "A sample vertex endpoint"
+  region       = "us-central1"
+  labels       = {
+    label-one = "value-one"
+  }
+  public_endpoint_enabled = true
+}
+
+data "google_project" "project" {}

vertex_ai_index_endpoint_deployed_index_automatic_resources/motd

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+===
+
+These examples use real resources that will be billed to the
+Google Cloud Platform project you use - so make sure that you
+run "terraform destroy" before quitting!
+
+===

vertex_ai_index_endpoint_deployed_index_automatic_resources/tutorial.md

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+# Vertex Ai Index Endpoint Deployed Index Automatic Resources - Terraform
+
+## Setup
+
+<walkthrough-author name="[email protected]" analyticsId="UA-125550242-1" tutorialName="vertex_ai_index_endpoint_deployed_index_automatic_resources" repositoryUrl="https://github.com/terraform-google-modules/docs-examples"></walkthrough-author>
+
+Welcome to Terraform in Google Cloud Shell! We need you to let us know what project you'd like to use with Terraform.
+
+<walkthrough-project-billing-setup></walkthrough-project-billing-setup>
+
+Terraform provisions real GCP resources, so anything you create in this session will be billed against this project.
+
+## Terraforming!
+
+Let's use {{project-id}} with Terraform! Click the Cloud Shell icon below to copy the command
+to your shell, and then run it from the shell by pressing Enter/Return. Terraform will pick up
+the project name from the environment variable.
+
+```bash
+export GOOGLE_CLOUD_PROJECT={{project-id}}
+```
+
+After that, let's get Terraform started. Run the following to pull in the providers.
+
+```bash
+terraform init
+```
+
+With the providers downloaded and a project set, you're ready to use Terraform. Go ahead!
+
+```bash
+terraform apply
+```
+
+Terraform will show you what it plans to do, and prompt you to accept. Type "yes" to accept the plan.
+
+```bash
+yes
+```
+
+
+## Post-Apply
+
+### Editing your config
+
+Now you've provisioned your resources in GCP! If you run a "plan", you should see no changes needed.
+
+```bash
+terraform plan
+```
+
+So let's make a change! Try editing a number, or appending a value to the name in the editor. Then,
+run a 'plan' again.
+
+```bash
+terraform plan
+```
+
+Afterwards you can run an apply, which implicitly does a plan and shows you the intended changes
+at the 'yes' prompt.
+
+```bash
+terraform apply
+```
+
+```bash
+yes
+```
+
+## Cleanup
+
+Run the following to remove the resources Terraform provisioned:
+
+```bash
+terraform destroy
+```
+```bash
+yes
+```

vertex_ai_index_endpoint_deployed_index_basic/backing_file.tf

@@ -0,0 +1,15 @@
+# This file has some scaffolding to make sure that names are unique and that
+# a region and zone are selected when you try to create your Terraform resources.
+
+locals {
+  name_suffix = "${random_pet.suffix.id}"
+}
+
+resource "random_pet" "suffix" {
+  length = 2
+}
+
+provider "google" {
+  region = "us-central1"
+  zone   = "us-central1-c"
+}

vertex_ai_index_endpoint_deployed_index_basic/main.tf

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+resource "google_service_account" "sa" {
+  account_id   = "vertex-sa-${local.name_suffix}"
+}
+
+resource "google_vertex_ai_index_endpoint_deployed_index" "basic_deployed_index" {
+  depends_on = [ google_vertex_ai_index_endpoint.vertex_index_endpoint_deployed, google_service_account.sa ]
+  index_endpoint = google_vertex_ai_index_endpoint.vertex_index_endpoint_deployed.id
+  index = google_vertex_ai_index.index.id // this is the index that will be deployed onto an endpoint
+  deployed_index_id = "deployed_index_id-${local.name_suffix}"
+  reserved_ip_ranges = ["vertex-ai-range-${local.name_suffix}"]
+  enable_access_logging = false
+  display_name = "vertex-deployed-index-${local.name_suffix}"
+  deployed_index_auth_config{
+    auth_provider{
+      audiences = ["123456-my-app"]
+      allowed_issuers = ["${google_service_account.sa.email}"]
+    }
+  }
+}
+
+resource "google_storage_bucket" "bucket" {
+  name     = "bucket-name-${local.name_suffix}"
+  location = "us-central1"
+  uniform_bucket_level_access = true
+}
+
+# The sample data comes from the following link:
+# https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#specify-namespaces-tokens
+resource "google_storage_bucket_object" "data" {
+  name   = "contents/data.json"
+  bucket = google_storage_bucket.bucket.name
+  content = <<EOF
+{"id": "42", "embedding": [0.5, 1.0], "restricts": [{"namespace": "class", "allow": ["cat", "pet"]},{"namespace": "category", "allow": ["feline"]}]}
+{"id": "43", "embedding": [0.6, 1.0], "restricts": [{"namespace": "class", "allow": ["dog", "pet"]},{"namespace": "category", "allow": ["canine"]}]}
+EOF
+}
+
+resource "google_vertex_ai_index" "index" {
+  labels = {
+    foo = "bar"
+  }
+  region   = "us-central1"
+  display_name = "test-index-${local.name_suffix}"
+  description = "index for test"
+  metadata {
+    contents_delta_uri = "gs://${google_storage_bucket.bucket.name}/contents"
+    config {
+      dimensions = 2
+      approximate_neighbors_count = 150
+      shard_size = "SHARD_SIZE_SMALL"
+      distance_measure_type = "DOT_PRODUCT_DISTANCE"
+      algorithm_config {
+        tree_ah_config {
+          leaf_node_embedding_count = 500
+          leaf_nodes_to_search_percent = 7
+        }
+      }
+    }
+  }
+  index_update_method = "BATCH_UPDATE"
+}
+
+resource "google_vertex_ai_index_endpoint" "vertex_index_endpoint_deployed" {
+  display_name = "sample-endpoint"
+  description  = "A sample vertex endpoint"
+  region       = "us-central1"
+  labels       = {
+    label-one = "value-one"
+  }
+  network      = "projects/${data.google_project.project.number}/global/networks/${data.google_compute_network.vertex_network.name}"
+}
+
+data "google_compute_network" "vertex_network" {
+  name       = "network-name-${local.name_suffix}"
+}
+
+data "google_project" "project" {}

vertex_ai_index_endpoint_deployed_index_basic/motd

@@ -0,0 +1,7 @@
+===
+
+These examples use real resources that will be billed to the
+Google Cloud Platform project you use - so make sure that you
+run "terraform destroy" before quitting!
+
+===

vertex_ai_index_endpoint_deployed_index_basic/tutorial.md

@@ -0,0 +1,79 @@
+# Vertex Ai Index Endpoint Deployed Index Basic - Terraform
+
+## Setup
+
+<walkthrough-author name="[email protected]" analyticsId="UA-125550242-1" tutorialName="vertex_ai_index_endpoint_deployed_index_basic" repositoryUrl="https://github.com/terraform-google-modules/docs-examples"></walkthrough-author>
+
+Welcome to Terraform in Google Cloud Shell! We need you to let us know what project you'd like to use with Terraform.
+
+<walkthrough-project-billing-setup></walkthrough-project-billing-setup>
+
+Terraform provisions real GCP resources, so anything you create in this session will be billed against this project.
+
+## Terraforming!
+
+Let's use {{project-id}} with Terraform! Click the Cloud Shell icon below to copy the command
+to your shell, and then run it from the shell by pressing Enter/Return. Terraform will pick up
+the project name from the environment variable.
+
+```bash
+export GOOGLE_CLOUD_PROJECT={{project-id}}
+```
+
+After that, let's get Terraform started. Run the following to pull in the providers.
+
+```bash
+terraform init
+```
+
+With the providers downloaded and a project set, you're ready to use Terraform. Go ahead!
+
+```bash
+terraform apply
+```
+
+Terraform will show you what it plans to do, and prompt you to accept. Type "yes" to accept the plan.
+
+```bash
+yes
+```
+
+
+## Post-Apply
+
+### Editing your config
+
+Now you've provisioned your resources in GCP! If you run a "plan", you should see no changes needed.
+
+```bash
+terraform plan
+```
+
+So let's make a change! Try editing a number, or appending a value to the name in the editor. Then,
+run a 'plan' again.
+
+```bash
+terraform plan
+```
+
+Afterwards you can run an apply, which implicitly does a plan and shows you the intended changes
+at the 'yes' prompt.
+
+```bash
+terraform apply
+```
+
+```bash
+yes
+```
+
+## Cleanup
+
+Run the following to remove the resources Terraform provisioned:
+
+```bash
+terraform destroy
+```
+```bash
+yes
+```

vertex_ai_index_endpoint_deployed_index_basic_two/backing_file.tf

@@ -0,0 +1,15 @@
+# This file has some scaffolding to make sure that names are unique and that
+# a region and zone are selected when you try to create your Terraform resources.
+
+locals {
+  name_suffix = "${random_pet.suffix.id}"
+}
+
+resource "random_pet" "suffix" {
+  length = 2
+}
+
+provider "google" {
+  region = "us-central1"
+  zone   = "us-central1-c"
+}