Vertex AI Search extension

Console

Register the extension

Perform the following steps to register the Vertex AI Search extension using the Google Cloud console.

1. In the Google Cloud console, go to the Vertex AI Extensions page.

   Go to Vertex AI Extensions

2. Click Create Extension.

3. In the Create a new extension dialog, fill in the following fields:

   • Extension name: Enter a name for your extension, such as "vertex_search_extension".
   • Description: (Optional) Enter an extension description, such as "A Vertex AI search extension".
   • Extension type: Select Vertex AI search.

4. In the OpenAPI Spec file section that now appears, confirm that the following fields are set correctly:

   • API name: vertex_ai_search.
   • API description: Performs search on user ingested data including website and unstructured data type...
   • Source: Cloud Storage.
   • OpenAPI Spec: vertex-extension-public/vertex_ai_search.yaml.
   • Authentication: Google service account.

5. In the Runtime configurations section, provide a serving configuration name. The serving configuration name is specified in the vertexAiSearchRuntimeConfig. It is formatted and completed as follows: projects/PROJECT_ID/locations/global/collections/COLLECTION_NAME/engines/ENGINE/servingConfigs/SERVING_CONFIG

   • Set COLLECTION_NAME to default_collection.
   • Set ENGINE to the application ID you received when you created your search application. To learn more, see Create a search app for website data.
   • Set SERVING_CONFIG to default_search.

6. Click Create Extension.

REST

Register the extension

Submit an Vertex AI API extensions.import request to register the Vertex AI Search extension.

Before using any of the request data, make the following replacements:

• PROJECT_ID: The ID of your Google Cloud project.
• REGION: A Compute Engine region.
• DISPLAY_NAME: The name extension that is displayed to users, such as "my_search_extension".
• DESCRIPTION: (Optional) The extension description that is displayed to users, such as "A search extension".
• SERVICE_ACCOUNT: (Optional) The Vertex AI Search extension uses GOOGLE_SERVICE_ACCOUNT_AUTH as shown in the sample request body. If you do not specify a service account, the extension uses the default Vertex AI Extension Service Agent service account. If you specify a different service account, grant the iam.serviceAccounts.getAccessToken permission to the Vertex AI Extension Service Agent service account on the specified service account.
• SERVING_CONFIG_NAME: The serving configuration name is specified in the vertexAiSearchRuntimeConfig. It is formatted and completed as follows: projects/PROJECT_ID/locations/global/collections/COLLECTION_NAME/engines/ENGINE/servingConfigs/SERVING_CONFIG.
  • PROJECT_ID: The ID of your Google Cloud project.
  • Set COLLECTION_NAME to default_collection.
  • ENGINE: The application ID you received when you created your search application. To learn more, see Create a search app for website data.
  • Set SERVING_CONFIG to default_search.
  The Cloud Storage bucket that the extension will use to read input files, including the gs:// prefix, for example, gs://sample-bucket-name. If specified, you must assign the roles/storage.objectViewer role on this bucket to the Vertex Extension Custom Code Service Agent.

HTTP method and URL:

POST http://REGION-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID/locations/REGION/extensions:import

Request JSON body:

{
  "displayName":"DISPLAY_NAME",
  "description":"DESCRIPTION",
  "manifest":{
    "name":"code_interpreter_tool",
    "description":"A Google Code Interpreter tool",
    "apiSpec":{
      "openApiGcsUri":"gs://vertex-extension-public/code_interpreter.yaml"
    },
    "authConfig":{
      "authType":"GOOGLE_SERVICE_ACCOUNT_AUTH",
      "googleServiceAccountConfig":{
        "serviceAccount":"SERVICE_ACCOUNT"
      }
    }
  }
  "runtimeConfig": {
     "vertexAiSearchRuntimeConfig": {
        "servingConfigName": "SERVING_CONFIG_NAME",
     }
  }
}

To send your request, choose one of these options:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "http://REGION-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID/locations/REGION/extensions:import"

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "http://REGION-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID/locations/REGION/extensions:import" | Select-Object -Expand Content

Run the extension

You can submit an execute operation to the Vertex AI API to obtain meaningful results from your data store.

POST http://REGION-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID/locations/REGION/extensions/EXTENSION_ID:execute

{
  "operation_id":"search",
  "operation_params":{
    "query":"Housing affordability since 2010",
  }
}

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "http://REGION-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID/locations/REGION/extensions/EXTENSION_ID:execute"

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "http://REGION-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID/locations/REGION/extensions/EXTENSION_ID:execute" | Select-Object -Expand Content