Purge data from a data store

This page describes how to purge all of the data in a structured or unstructured data store.

You must purge the data in a structured, unstructured, or healthcare data store before you can delete the data store.

You can also purge the data in a data store if you want to completely delete the contents of the data store before re-importing fresh data. Purging a data store deletes only the data in the data store, leaving your app, schema, and configurations intact.

Website data stores

Purging is not an option for website data stores. You can remove websites from website data stores as needed, but this is not required before deleting the data store.

Note: It can take from 6 to 24 hours to remove websites from website data stores with advanced website indexing.

Purge data

To purge data from a data store, do the following:

Console

To use the Google Cloud console to purge the data from a branch of a structured, unstructured, or healthcare data store, follow these steps:

1. In the Google Cloud console, go to the Agent Builder page.

   Agent Builder

2. In the navigation menu, click Data Stores.

3. In the Name column, click the data store that you want to purge.

4. In the Documents tab, click Purge data.

5. Read the warning in the Confirm purge data dialog. If you want to continue, enter the name of your data store, and then click Confirm. Purging data is a long-running operation. For more information, see Monitor long-running operations.

6. Click the Activity tab to monitor the progress of the purge operation.

REST

To use the command line to purge the data from a branch of a structured or unstructured data store, follow these steps:

1. Find your data store ID. If you already have your data store ID, skip to the next step.

   1. In the Google Cloud console, go to the Agent Builder page and in the navigation menu, click Data Stores.

      Go to the Data Stores page

   2. Click the name of your data store.

   3. On the Data page for your data store, get the data store ID.

2. Call the documents.purge method.

   curl -X POST \
   -H "Authorization: Bearer $(gcloud auth print-access-token)" \
   -H "Content-Type: application/json" \
   "https://discoveryengine.googleapis.com/v1/projects/PROJECT_ID/locations/global/collections/default_collection/dataStores/DATA_STORE_ID/branches/0/documents:purge" \
   -d '{
     "filter": "*",
     "force": FORCE
   }'
   

   • PROJECT_ID: Google Cloud project.
   • DATA_STORE_ID: the ID of the Vertex AI Search data store..
   • FORCE: a boolean value that specifies whether to delete data from the branch of the data store.
     • If true, deletes all data from the branch
     • If false, deletes no data and returns a list of documents in the branch.
     • If force is omitted, the default is false.

3. Optional: Make note of the name value returned by the documents.purge method and follow the instructions in Get details about a long-running operation to see when the purge operation is complete.

C#

For more information, see the Vertex AI Agent Builder C# API reference documentation.

To authenticate to Vertex AI Agent Builder, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

using Google.Cloud.DiscoveryEngine.V1;
using Google.LongRunning;

public sealed partial class GeneratedDocumentServiceClientSnippets
{
    /// <summary>Snippet for PurgeDocuments</summary>
    /// <remarks>
    /// This snippet has been automatically generated and should be regarded as a code template only.
    /// It will require modifications to work:
    /// - It may require correct/in-range values for request initialization.
    /// - It may require specifying regional endpoints when creating the service client as shown in
    ///   https://cloud.google.com/dotnet/docs/reference/help/client-configuration#endpoint.
    /// </remarks>
    public void PurgeDocumentsRequestObject()
    {
        // Create client
        DocumentServiceClient documentServiceClient = DocumentServiceClient.Create();
        // Initialize request argument(s)
        PurgeDocumentsRequest request = new PurgeDocumentsRequest
        {
            ParentAsBranchName = BranchName.FromProjectLocationDataStoreBranch("[PROJECT]", "[LOCATION]", "[DATA_STORE]", "[BRANCH]"),
            Filter = "",
            Force = false,
            GcsSource = new GcsSource(),
            ErrorConfig = new PurgeErrorConfig(),
        };
        // Make the request
        Operation<PurgeDocumentsResponse, PurgeDocumentsMetadata> response = documentServiceClient.PurgeDocuments(request);

        // Poll until the returned long-running operation is complete
        Operation<PurgeDocumentsResponse, PurgeDocumentsMetadata> completedResponse = response.PollUntilCompleted();
        // Retrieve the operation result
        PurgeDocumentsResponse result = completedResponse.Result;

        // Or get the name of the operation
        string operationName = response.Name;
        // This name can be stored, then the long-running operation retrieved later by name
        Operation<PurgeDocumentsResponse, PurgeDocumentsMetadata> retrievedResponse = documentServiceClient.PollOncePurgeDocuments(operationName);
        // Check if the retrieved long-running operation has completed
        if (retrievedResponse.IsCompleted)
        {
            // If it has completed, then access the result
            PurgeDocumentsResponse retrievedResult = retrievedResponse.Result;
        }
    }
}

Go

For more information, see the Vertex AI Agent Builder Go API reference documentation.

To authenticate to Vertex AI Agent Builder, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

package main

import (
	"context"

	discoveryengine "cloud.google.com/go/discoveryengine/apiv1"
	discoveryenginepb "cloud.google.com/go/discoveryengine/apiv1/discoveryenginepb"
)

func main() {
	ctx := context.Background()
	// This snippet has been automatically generated and should be regarded as a code template only.
	// It will require modifications to work:
	// - It may require correct/in-range values for request initialization.
	// - It may require specifying regional endpoints when creating the service client as shown in:
	//   https://pkg.go.dev/cloud.google.com/go#hdr-Client_Options
	c, err := discoveryengine.NewDocumentClient(ctx)
	if err != nil {
		// TODO: Handle error.
	}
	defer c.Close()

	req := &discoveryenginepb.PurgeDocumentsRequest{
		// TODO: Fill request struct fields.
		// See https://pkg.go.dev/cloud.google.com/go/discoveryengine/apiv1/discoveryenginepb#PurgeDocumentsRequest.
	}
	op, err := c.PurgeDocuments(ctx, req)
	if err != nil {
		// TODO: Handle error.
	}

	resp, err := op.Wait(ctx)
	if err != nil {
		// TODO: Handle error.
	}
	// TODO: Use resp.
	_ = resp
}

Java

For more information, see the Vertex AI Agent Builder Java API reference documentation.

To authenticate to Vertex AI Agent Builder, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

import com.google.cloud.discoveryengine.v1.BranchName;
import com.google.cloud.discoveryengine.v1.DocumentServiceClient;
import com.google.cloud.discoveryengine.v1.PurgeDocumentsRequest;
import com.google.cloud.discoveryengine.v1.PurgeDocumentsResponse;
import com.google.cloud.discoveryengine.v1.PurgeErrorConfig;

public class SyncPurgeDocuments {

  public static void main(String[] args) throws Exception {
    syncPurgeDocuments();
  }

  public static void syncPurgeDocuments() throws Exception {
    // This snippet has been automatically generated and should be regarded as a code template only.
    // It will require modifications to work:
    // - It may require correct/in-range values for request initialization.
    // - It may require specifying regional endpoints when creating the service client as shown in
    // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
    try (DocumentServiceClient documentServiceClient = DocumentServiceClient.create()) {
      PurgeDocumentsRequest request =
          PurgeDocumentsRequest.newBuilder()
              .setParent(
                  BranchName.ofProjectLocationDataStoreBranchName(
                          "[PROJECT]", "[LOCATION]", "[DATA_STORE]", "[BRANCH]")
                      .toString())
              .setFilter("filter-1274492040")
              .setErrorConfig(PurgeErrorConfig.newBuilder().build())
              .setForce(true)
              .build();
      PurgeDocumentsResponse response = documentServiceClient.purgeDocumentsAsync(request).get();
    }
  }
}

Node.js

For more information, see the Vertex AI Agent Builder Node.js API reference documentation.

To authenticate to Vertex AI Agent Builder, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

/**
 * This snippet has been automatically generated and should be regarded as a code template only.
 * It will require modifications to work.
 * It may require correct/in-range values for request initialization.
 * TODO(developer): Uncomment these variables before running the sample.
 */
/**
 *  Cloud Storage location for the input content.
 *  Supported `data_schema`:
 *  * `document_id`: One valid
 *  Document.id google.cloud.discoveryengine.v1.Document.id  per line.
 */
// const gcsSource = {}
/**
 *  Inline source for the input content for purge.
 */
// const inlineSource = {}
/**
 *  Required. The parent resource name, such as
 *  `projects/{project}/locations/{location}/collections/{collection}/dataStores/{data_store}/branches/{branch}`.
 */
// const parent = 'abc123'
/**
 *  Required. Filter matching documents to purge. Only currently supported
 *  value is
 *  `*` (all items).
 */
// const filter = 'abc123'
/**
 *  The desired location of errors incurred during the purge.
 */
// const errorConfig = {}
/**
 *  Actually performs the purge. If `force` is set to false, return the
 *  expected purge count without deleting any documents.
 */
// const force = true

// Imports the Discoveryengine library
const {DocumentServiceClient} = require('@google-cloud/discoveryengine').v1;

// Instantiates a client
const discoveryengineClient = new DocumentServiceClient();

async function callPurgeDocuments() {
  // Construct request
  const request = {
    parent,
    filter,
  };

  // Run request
  const [operation] = await discoveryengineClient.purgeDocuments(request);
  const [response] = await operation.promise();
  console.log(response);
}

callPurgeDocuments();

Python

For more information, see the Vertex AI Agent Builder Python API reference documentation.

To authenticate to Vertex AI Agent Builder, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

from google.api_core.client_options import ClientOptions
from google.cloud import discoveryengine

# TODO(developer): Uncomment these variables before running the sample.
# project_id = "YOUR_PROJECT_ID"
# location = "YOUR_LOCATION"            # Values: "global", "us", "eu"
# data_store_id = "YOUR_DATA_STORE_ID"


def purge_documents_sample(
    project_id: str, location: str, data_store_id: str
) -> discoveryengine.PurgeDocumentsMetadata:
    #  For more information, refer to:
    # https://cloud.google.com/generative-ai-app-builder/docs/locations#specify_a_multi-region_for_your_data_store
    client_options = (
        ClientOptions(api_endpoint=f"{location}-discoveryengine.googleapis.com")
        if location != "global"
        else None
    )

    # Create a client
    client = discoveryengine.DocumentServiceClient(client_options=client_options)

    operation = client.purge_documents(
        request=discoveryengine.PurgeDocumentsRequest(
            # The full resource name of the search engine branch.
            # e.g. projects/{project}/locations/{location}/dataStores/{data_store_id}/branches/{branch}
            parent=client.branch_path(
                project=project_id,
                location=location,
                data_store=data_store_id,
                branch="default_branch",
            ),
            filter="*",
            # If force is set to `False`, return the expected purge count without deleting any documents.
            force=True,
        )
    )

    print(f"Waiting for operation to complete: {operation.operation.name}")
    response = operation.result()

    # After the operation is complete,
    # get information from operation metadata
    metadata = discoveryengine.PurgeDocumentsMetadata(operation.metadata)

    # Handle the response
    print(response)
    print(metadata)

    return metadata

Ruby

For more information, see the Vertex AI Agent Builder Ruby API reference documentation.

To authenticate to Vertex AI Agent Builder, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

require "google/cloud/discovery_engine/v1"

##
# Snippet for the purge_documents call in the DocumentService service
#
# This snippet has been automatically generated and should be regarded as a code
# template only. It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in https://cloud.google.com/ruby/docs/reference.
#
# This is an auto-generated example demonstrating basic usage of
# Google::Cloud::DiscoveryEngine::V1::DocumentService::Client#purge_documents.
#
def purge_documents
  # Create a client object. The client can be reused for multiple calls.
  client = Google::Cloud::DiscoveryEngine::V1::DocumentService::Client.new

  # Create a request. To set request fields, pass in keyword arguments.
  request = Google::Cloud::DiscoveryEngine::V1::PurgeDocumentsRequest.new

  # Call the purge_documents method.
  result = client.purge_documents request

  # The returned object is of type Gapic::Operation. You can use it to
  # check the status of an operation, cancel it, or wait for results.
  # Here is how to wait for a response.
  result.wait_until_done! timeout: 60
  if result.response?
    p result.response
  else
    puts "No response received."
  end
end