パブリック インデックス エンドポイントのデプロイと管理

resource "google_vertex_ai_index_endpoint" "default" {   display_name            = "sample-endpoint"   description             = "A sample index endpoint with a public endpoint"   region                  = "us-central1"   public_endpoint_enabled = true }  # Cloud Storage bucket name must be unique resource "random_id" "default" {   byte_length = 8 }  # Create a Cloud Storage bucket resource "google_storage_bucket" "bucket" {   name                        = "vertex-ai-index-bucket-${random_id.default.hex}"   location                    = "us-central1"   uniform_bucket_level_access = true }  # Create index content resource "google_storage_bucket_object" "data" {   name    = "contents/data.json"   bucket  = google_storage_bucket.bucket.name   content = <<EOF {"id": "42", "embedding": [0.5, 1.0], "restricts": [{"namespace": "class", "allow": ["cat", "pet"]},{"namespace": "category", "allow": ["feline"]}]} {"id": "43", "embedding": [0.6, 1.0], "restricts": [{"namespace": "class", "allow": ["dog", "pet"]},{"namespace": "category", "allow": ["canine"]}]} EOF }  resource "google_vertex_ai_index" "default" {   region       = "us-central1"   display_name = "sample-index-batch-update"   description  = "A sample index for batch update"   labels = {     foo = "bar"   }    metadata {     contents_delta_uri = "gs://${google_storage_bucket.bucket.name}/contents"     config {       dimensions                  = 2       approximate_neighbors_count = 150       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"    timeouts {     create = "2h"     update = "1h"   } }

def vector_search_create_index_endpoint(     project: str, location: str, display_name: str ) -> None:     """Create a vector search index endpoint.      Args:         project (str): Required. Project ID         location (str): Required. The region name         display_name (str): Required. The index endpoint display name     """     # Initialize the Vertex AI client     aiplatform.init(project=project, location=location)      # Create Index Endpoint     index_endpoint = aiplatform.MatchingEngineIndexEndpoint.create(         display_name=display_name,         public_endpoint_enabled=True,         description="Matching Engine Index Endpoint",     )      print(index_endpoint.name)  

provider "google" {   region = "us-central1" }  resource "google_vertex_ai_index_endpoint_deployed_index" "default" {   depends_on        = [google_vertex_ai_index_endpoint.default]   index_endpoint    = google_vertex_ai_index_endpoint.default.id   index             = google_vertex_ai_index.default.id   deployed_index_id = "deployed_index_id" }  resource "google_vertex_ai_index_endpoint" "default" {   display_name            = "sample-endpoint"   description             = "A sample index endpoint with a public endpoint"   region                  = "us-central1"   public_endpoint_enabled = true }  # Cloud Storage bucket name must be unique resource "random_id" "default" {   byte_length = 8 }  # Create a Cloud Storage bucket resource "google_storage_bucket" "bucket" {   name                        = "vertex-ai-index-bucket-${random_id.default.hex}"   location                    = "us-central1"   uniform_bucket_level_access = true }  # Create index content resource "google_storage_bucket_object" "data" {   name    = "contents/data.json"   bucket  = google_storage_bucket.bucket.name   content = <<EOF {"id": "42", "embedding": [0.5, 1.0], "restricts": [{"namespace": "class", "allow": ["cat", "pet"]},{"namespace": "category", "allow": ["feline"]}]} {"id": "43", "embedding": [0.6, 1.0], "restricts": [{"namespace": "class", "allow": ["dog", "pet"]},{"namespace": "category", "allow": ["canine"]}]} EOF }  resource "google_vertex_ai_index" "default" {   region       = "us-central1"   display_name = "sample-index-batch-update"   description  = "A sample index for batch update"   labels = {     foo = "bar"   }    metadata {     contents_delta_uri = "gs://${google_storage_bucket.bucket.name}/contents"     config {       dimensions                  = 2       approximate_neighbors_count = 150       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"    timeouts {     create = "2h"     update = "1h"   } }

def vector_search_deploy_index(     project: str,     location: str,     index_name: str,     index_endpoint_name: str,     deployed_index_id: str, ) -> None:     """Deploy a vector search index to a vector search index endpoint.      Args:         project (str): Required. Project ID         location (str): Required. The region name         index_name (str): Required. The index to update. A fully-qualified index           resource name or a index ID.  Example:           "projects/123/locations/us-central1/indexes/my_index_id" or           "my_index_id".         index_endpoint_name (str): Required. Index endpoint to deploy the index           to.         deployed_index_id (str): Required. The user specified ID of the           DeployedIndex.     """     # Initialize the Vertex AI client     aiplatform.init(project=project, location=location)      # Create the index instance from an existing index     index = aiplatform.MatchingEngineIndex(index_name=index_name)      # Create the index endpoint instance from an existing endpoint.     index_endpoint = aiplatform.MatchingEngineIndexEndpoint(         index_endpoint_name=index_endpoint_name     )      # Deploy Index to Endpoint     index_endpoint = index_endpoint.deploy_index(         index=index, deployed_index_id=deployed_index_id     )      print(index_endpoint.deployed_indexes)  

def vector_search_deploy_autoscaling_index(     project: str,     location: str,     index_name: str,     index_endpoint_name: str,     deployed_index_id: str,     min_replica_count: int,     max_replica_count: int, ) -> None:     """Deploy a vector search index to a vector search index endpoint.      Args:         project (str): Required. Project ID         location (str): Required. The region name         index_name (str): Required. The index to update. A fully-qualified index           resource name or a index ID.  Example:           "projects/123/locations/us-central1/indexes/my_index_id" or           "my_index_id".         index_endpoint_name (str): Required. Index endpoint to deploy the index           to.         deployed_index_id (str): Required. The user specified ID of the           DeployedIndex.         min_replica_count (int): Required. The minimum number of replicas to           deploy.         max_replica_count (int): Required. The maximum number of replicas to           deploy.     """     # Initialize the Vertex AI client     aiplatform.init(project=project, location=location)      # Create the index instance from an existing index     index = aiplatform.MatchingEngineIndex(index_name=index_name)      # Create the index endpoint instance from an existing endpoint.     index_endpoint = aiplatform.MatchingEngineIndexEndpoint(         index_endpoint_name=index_endpoint_name     )      # Deploy Index to Endpoint. Specifying min and max replica counts will     # enable autoscaling.     index_endpoint.deploy_index(         index=index,         deployed_index_id=deployed_index_id,         min_replica_count=min_replica_count,         max_replica_count=max_replica_count,     )  

provider "google" {   region = "us-central1" }  resource "google_vertex_ai_index_endpoint_deployed_index" "default" {   depends_on        = [google_vertex_ai_index_endpoint.default]   index_endpoint    = google_vertex_ai_index_endpoint.default.id   index             = google_vertex_ai_index.default.id   deployed_index_id = "deployed_index_for_mutate"   # This example assumes the deployed index endpoint's resources configuration   # differs from the values specified below. Terraform will mutate the deployed   # index endpoint's resource configuration to match.   automatic_resources {     min_replica_count = 3     max_replica_count = 5   } }  resource "google_vertex_ai_index_endpoint" "default" {   display_name            = "sample-endpoint"   description             = "A sample index endpoint with a public endpoint"   region                  = "us-central1"   public_endpoint_enabled = true }  # Cloud Storage bucket name must be unique resource "random_id" "default" {   byte_length = 8 }  # Create a Cloud Storage bucket resource "google_storage_bucket" "bucket" {   name                        = "vertex-ai-index-bucket-${random_id.default.hex}"   location                    = "us-central1"   uniform_bucket_level_access = true }  # Create index content resource "google_storage_bucket_object" "data" {   name    = "contents/data.json"   bucket  = google_storage_bucket.bucket.name   content = <<EOF {"id": "42", "embedding": [0.5, 1.0], "restricts": [{"namespace": "class", "allow": ["cat", "pet"]},{"namespace": "category", "allow": ["feline"]}]} {"id": "43", "embedding": [0.6, 1.0], "restricts": [{"namespace": "class", "allow": ["dog", "pet"]},{"namespace": "category", "allow": ["canine"]}]} EOF }  resource "google_vertex_ai_index" "default" {   region       = "us-central1"   display_name = "sample-index-batch-update"   description  = "A sample index for batch update"   labels = {     foo = "bar"   }    metadata {     contents_delta_uri = "gs://${google_storage_bucket.bucket.name}/contents"     config {       dimensions                  = 2       approximate_neighbors_count = 150       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"    timeouts {     create = "2h"     update = "1h"   } }

def vector_search_mutate_deployed_index(     project: str,     location: str,     index_endpoint_name: str,     deployed_index_id: str,     min_replica_count: int,     max_replica_count: int, ) -> None:     """Mutate the deployment resources of an already deployed index.      Args:         project (str): Required. Project ID         location (str): Required. The region name         index_endpoint_name (str): Required. Index endpoint to run the query           against.         deployed_index_id (str): Required. The ID of the DeployedIndex to run           the queries against.         min_replica_count (int): Required. The minimum number of replicas to           deploy.         max_replica_count (int): Required. The maximum number of replicas to           deploy.     """     # Initialize the Vertex AI client     aiplatform.init(project=project, location=location)      # Create the index endpoint instance from an existing endpoint     index_endpoint = aiplatform.MatchingEngineIndexEndpoint(         index_endpoint_name=index_endpoint_name     )      # Mutate the deployed index     index_endpoint.mutate_deployed_index(         deployed_index_id=deployed_index_id,         min_replica_count=min_replica_count,         max_replica_count=max_replica_count,     )  

def vector_search_list_index_endpoint(     project: str, location: str ) -> List[aiplatform.MatchingEngineIndexEndpoint]:     """List vector search index endpoints.      Args:         project (str): Required. Project ID         location (str): Required. The region name      Returns:         List of aiplatform.MatchingEngineIndexEndpoint     """     # Initialize the Vertex AI client     aiplatform.init(project=project, location=location)      # List Index Endpoints     return aiplatform.MatchingEngineIndexEndpoint.list()  

def vector_search_undeploy_index(     project: str,     location: str,     index_endpoint_name: str,     deployed_index_id: str, ) -> None:     """Mutate the deployment resources of an already deployed index.      Args:         project (str): Required. Project ID         location (str): Required. The region name         index_endpoint_name (str): Required. Index endpoint to run the query           against.         deployed_index_id (str): Required. The ID of the DeployedIndex to run           the queries against.     """     # Initialize the Vertex AI client     aiplatform.init(project=project, location=location)      # Create the index endpoint instance from an existing endpoint     index_endpoint = aiplatform.MatchingEngineIndexEndpoint(         index_endpoint_name=index_endpoint_name     )      # Undeploy the index     index_endpoint.undeploy_index(         deployed_index_id=deployed_index_id,     )  

def vector_search_delete_index_endpoint(     project: str, location: str, index_endpoint_name: str, force: bool = False ) -> None:     """Delete a vector search index endpoint.      Args:         project (str): Required. Project ID         location (str): Required. The region name         index_endpoint_name (str): Required. Index endpoint to run the query           against.         force (bool): Required. If true, undeploy any deployed indexes on this           endpoint before deletion.     """     # Initialize the Vertex AI client     aiplatform.init(project=project, location=location)      # Create the index endpoint instance from an existing endpoint     index_endpoint = aiplatform.MatchingEngineIndexEndpoint(         index_endpoint_name=index_endpoint_name     )      # Delete the index endpoint     index_endpoint.delete(force=force)