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Add Terraform support for Vector Search 2.0 Collection (#16335) (#26098)
[upstream:56ae742f56aff39782b865a07d90a3a7c08fd548] Signed-off-by: Modular Magician <magic-modules@google.com>
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‎.changelog/16335.txt‎

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```release-note:new-resource
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`google_vector_search_collection`
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```

‎.teamcity/components/inputs/services_beta.kt‎

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"displayName" to "Transcoder",
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"path" to "./google-beta/services/transcoder"
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),
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"vectorsearch" to mapOf(
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"name" to "vectorsearch",
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"displayName" to "VectorSearch",
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"path" to "./google-beta/services/vectorsearch",
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),
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"vertexai" to mapOf(
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"name" to "vertexai",
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"displayName" to "Vertexai",

‎.teamcity/components/inputs/services_ga.kt‎

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"displayName" to "Transcoder",
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"path" to "./google/services/transcoder"
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),
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"vectorsearch" to mapOf(
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"name" to "vectorsearch",
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"displayName" to "VectorSearch",
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"path" to "./google/services/vectorsearch",
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),
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"vertexai" to mapOf(
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"name" to "vertexai",
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"displayName" to "Vertexai",

‎google/services/vectorsearch/resource_vector_search_collection_test.go‎

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// Copyright (c) HashiCorp, Inc.
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// SPDX-License-Identifier: MPL-2.0
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// ----------------------------------------------------------------------------
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//
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// *** AUTO GENERATED CODE *** Type: Handwritten ***
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//
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// ----------------------------------------------------------------------------
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//
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// This code is generated by Magic Modules using the following:
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//
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// Source file: https://github.com/GoogleCloudPlatform/magic-modules/tree/main/mmv1/third_party/terraform/services/vectorsearch/resource_vector_search_collection_test.go.tmpl
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//
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// DO NOT EDIT this file directly. Any changes made to this file will be
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// overwritten during the next generation cycle.
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//
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// ----------------------------------------------------------------------------
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package vectorsearch_test

‎website/docs/r/vector_search_collection.html.markdown‎

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---
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# ----------------------------------------------------------------------------
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#
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# *** AUTO GENERATED CODE *** Type: MMv1 ***
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#
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# ----------------------------------------------------------------------------
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#
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# This code is generated by Magic Modules using the following:
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#
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# Configuration: https:#github.com/GoogleCloudPlatform/magic-modules/tree/main/mmv1/products/vectorsearch/Collection.yaml
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# Template: https:#github.com/GoogleCloudPlatform/magic-modules/tree/main/mmv1/templates/terraform/resource.html.markdown.tmpl
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#
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# DO NOT EDIT this file directly. Any changes made to this file will be
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# overwritten during the next generation cycle.
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#
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# ----------------------------------------------------------------------------
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subcategory: "Vector Search"
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description: |-
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Description
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---
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# google_vector_search_collection
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Description
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~> **Warning:** This resource is in beta, and should be used with the terraform-provider-google-beta provider.
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See [Provider Versions](../guides/provider_versions.html.markdown) for more details on beta resources.
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<div class = "oics-button" style="float: right; margin: 0 0 -15px">
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<a href="https://console.cloud.google.com/cloudshell/open?cloudshell_git_repo=https%3A%2F%2Fgithub.com%2Fterraform-google-modules%2Fdocs-examples.git&cloudshell_image=gcr.io%2Fcloudshell-images%2Fcloudshell%3Alatest&cloudshell_print=.%2Fmotd&cloudshell_tutorial=.%2Ftutorial.md&cloudshell_working_dir=vectorsearch_collection_basic&open_in_editor=main.tf" target="_blank">
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<img alt="Open in Cloud Shell" src="//gstatic.com/cloudssh/images/open-btn.svg" style="max-height: 44px; margin: 32px auto; max-width: 100%;">
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</a>
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</div>
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## Example Usage - Vectorsearch Collection Basic
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```hcl
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resource "google_vector_search_collection" "example-collection" {
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provider = google-beta
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location = "us-central1"
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collection_id = "example-collection"
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display_name = "My Awesome Collection"
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description = "This collection stores important data."
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labels = {
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env = "dev"
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team = "my-team"
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}
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data_schema = <<EOF
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{
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"type": "object",
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"properties": {
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"title": {
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"type": "string"
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},
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"plot": {
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"type": "string"
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}
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}
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}
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EOF
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vector_schema {
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field_name = "text_embedding"
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dense_vector {
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dimensions = 768
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vertex_embedding_config {
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model_id = "textembedding-gecko@003"
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task_type = "RETRIEVAL_DOCUMENT"
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text_template = "Title: {title} ---- Plot: {plot}"
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}
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}
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}
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}
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```
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## Argument Reference
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The following arguments are supported:
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* `location` -
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(Required)
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Resource ID segment making up resource `name`. It identifies the resource within its parent collection as described in https://google.aip.dev/122.
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* `collection_id` -
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(Required)
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ID of the Collection to create.
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The id must be 1-63 characters long, and comply with
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[RFC1035](https://www.ietf.org/rfc/rfc1035.txt).
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Specifically, it must be 1-63 characters long and match the regular
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expression `[a-z](?:[-a-z0-9]{0,61}[a-z0-9])?`.
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* `data_schema` -
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(Optional)
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JSON Schema for data.
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Field names must contain only alphanumeric characters,
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underscores, and hyphens.
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* `description` -
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(Optional)
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User-specified description of the collection
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* `display_name` -
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(Optional)
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User-specified display name of the collection
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* `labels` -
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(Optional)
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Labels as key value pairs.
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**Note**: This field is non-authoritative, and will only manage the labels present in your configuration.
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Please refer to the field `effective_labels` for all of the labels present on the resource.
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* `vector_schema` -
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(Optional)
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Schema for vector fields. Only vector fields in this schema will be
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searchable.
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Field names must contain only alphanumeric characters,
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underscores, and hyphens.
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Structure is [documented below](#nested_vector_schema).
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* `project` - (Optional) The ID of the project in which the resource belongs.
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If it is not provided, the provider project is used.
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<a name="nested_vector_schema"></a>The `vector_schema` block supports:
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* `field_name` - (Required) The identifier for this object. Format specified above.
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* `dense_vector` -
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(Optional)
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Message describing a dense vector field.
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Structure is [documented below](#nested_vector_schema_dense_vector).
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* `sparse_vector` -
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(Optional)
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Message describing a sparse vector field.
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<a name="nested_vector_schema_dense_vector"></a>The `dense_vector` block supports:
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* `dimensions` -
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(Optional)
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Dimensionality of the vector field.
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* `vertex_embedding_config` -
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(Optional)
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Message describing the configuration for generating embeddings for a vector
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field using Vertex AI embeddings API.
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Structure is [documented below](#nested_vector_schema_dense_vector_vertex_embedding_config).
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<a name="nested_vector_schema_dense_vector_vertex_embedding_config"></a>The `vertex_embedding_config` block supports:
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* `model_id` -
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(Required)
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Required: ID of the embedding model to use. See
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https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#embeddings-models
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for the list of supported models.
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* `task_type` -
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(Required)
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Possible values:
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RETRIEVAL_QUERY
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RETRIEVAL_DOCUMENT
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SEMANTIC_SIMILARITY
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CLASSIFICATION
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CLUSTERING
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QUESTION_ANSWERING
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FACT_VERIFICATION
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CODE_RETRIEVAL_QUERY
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* `text_template` -
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(Required)
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Required: Text template for the input to the model. The template must
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contain one or more references to fields in the DataObject, e.g.:
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"Movie Title: {title} ---- Movie Plot: {plot}".
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## Attributes Reference
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In addition to the arguments listed above, the following computed attributes are exported:
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* `id` - an identifier for the resource with format `projects/{{project}}/locations/{{location}}/collections/{{collection_id}}`
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* `create_time` -
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[Output only] Create time stamp
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* `name` -
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Identifier. name of resource
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* `update_time` -
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[Output only] Update time stamp
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* `terraform_labels` -
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The combination of labels configured directly on the resource
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and default labels configured on the provider.
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* `effective_labels` -
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All of labels (key/value pairs) present on the resource in GCP, including the labels configured through Terraform, other clients and services.
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## Timeouts
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This resource provides the following
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[Timeouts](https://developer.hashicorp.com/terraform/plugin/sdkv2/resources/retries-and-customizable-timeouts) configuration options:
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- `create` - Default is 20 minutes.
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- `update` - Default is 20 minutes.
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- `delete` - Default is 20 minutes.
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## Import
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Collection can be imported using any of these accepted formats:
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* `projects/{{project}}/locations/{{location}}/collections/{{collection_id}}`
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* `{{project}}/{{location}}/{{collection_id}}`
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* `{{location}}/{{collection_id}}`
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In Terraform v1.5.0 and later, use an [`import` block](https://developer.hashicorp.com/terraform/language/import) to import Collection using one of the formats above. For example:
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```tf
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import {
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id = "projects/{{project}}/locations/{{location}}/collections/{{collection_id}}"
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to = google_vector_search_collection.default
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}
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```
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When using the [`terraform import` command](https://developer.hashicorp.com/terraform/cli/commands/import), Collection can be imported using one of the formats above. For example:
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```
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$ terraform import google_vector_search_collection.default projects/{{project}}/locations/{{location}}/collections/{{collection_id}}
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$ terraform import google_vector_search_collection.default {{project}}/{{location}}/{{collection_id}}
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$ terraform import google_vector_search_collection.default {{location}}/{{collection_id}}
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```
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## User Project Overrides
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This resource supports [User Project Overrides](https://registry.terraform.io/providers/hashicorp/google/latest/docs/guides/provider_reference#user_project_override).

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