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=head1 OpenSearch::Client::Manual::Modules

Most of the methods available in OpenSearch::Client are accessed through different namespaces.
Each of these namespaces is served by a specific module.

You can lookup which C<modulename-E<gt>methodname> corresponds to each path in the OpenSearch API documentation
in L<OpenSearch::Client::Manual::Paths|OpenSearch::Client::Core::3_0::Manual::Paths>

=head1 Namespaces

=head2 asynchronous_search

Searching large volumes of data can take a long time, especially if you're searching across warm nodes or multiple remote clusters. Asynchronous search in OpenSearch lets you send search requests that run in the background. You can monitor the progre...

Module : L<OpenSearch::Client::Core::3_0::Direct::AsyncSearch>

=head2 cat

You can get essential statistics about your cluster in an easy-to-understand, tabular format using the compact and aligned text (CAT) API. The CAT API is a human-readable interface that returns plain text instead of traditional JSON. Using the CAT AP...

Module : L<OpenSearch::Client::Core::3_0::Direct::Cat>

=head2 cluster

The cluster APIs allow you to manage your cluster. You can use them to check cluster health, modify settings, retrieve statistics, and more.

Module : L<OpenSearch::Client::Core::3_0::Direct::Cluster>

=head2 dangling_indices

After a node joins a cluster, dangling indexes occur if any shards exist in the node's local directory that do not already exist in the cluster. Dangling indexes can be listed, deleted, or imported.

Module : L<OpenSearch::Client::Core::3_0::Direct::DanglingIndices>

=head2 flow_framework

You can automate complex OpenSearch setup and preprocessing tasks by providing templates for common use cases. For example, automating machine learning (ML) setup tasks streamlines the use of OpenSearch ML offerings.

Module : L<OpenSearch::Client::Core::3_0::Direct::FlowFramework>

=head2 geospatial

The ip2geo processor adds information about the geographical location of an IPv4 or IPv6 address. The ip2geo processor uses IP geolocation (GeoIP) data from an external endpoint and therefore requires an additional component, datasource, that defines...

Module : L<OpenSearch::Client::Core::3_0::Direct::GeoSpatial>

=head2 indices

The index API operations let you interact with indexes in your cluster. Using these operations, you can create, delete, close, and complete other index-related operations.

Module : L<OpenSearch::Client::Core::3_0::Direct::Indices>

=head2 ingest

Ingest APIs are a valuable tool for loading data into a system. Ingest APIs work together with ingest pipelines and ingest processors to process or transform data from a variety of sources and in a variety of formats.

Module : L<OpenSearch::Client::Core::3_0::Direct::Ingest>

=head2 ingestion

Pull-based ingestion enables OpenSearch to ingest data from streaming sources such as Apache Kafka or Amazon Kinesis. Unlike traditional ingestion methods where clients actively push data to OpenSearch through REST APIs, pull-based ingestion allows O...

Module : L<OpenSearch::Client::Core::3_0::Direct::Ingestion>

=head2 insights

To monitor and analyze the search queries within your OpenSearch cluster, you can obtain query insights.

Module : L<OpenSearch::Client::Core::3_0::Direct::Insights>

=head2 ism

Use the index state management operations to programmatically work with policies and managed indexes.

Module : L<OpenSearch::Client::Core::3_0::Direct::ISM>

=head2 knn

In OpenSearch, vector search functionality is provided by the k-NN plugin and Neural Search plugin. The k-NN plugin provides basic k-NN functionality, while the Neural Search plugin provides automatic embedding generation at indexing and search time.

Module : L<OpenSearch::Client::Core::3_0::Direct::KNN>

=head2 list

The List API retrieves statistics about indexes and shards in a paginated format. This streamlines the task of processing responses that include many indexes.

Module : L<OpenSearch::Client::Core::3_0::Direct::List>

=head2 ltr

The Learning to Rank plugin for OpenSearch enables you to use machine learning (ML) and behavioral data to fine-tune the relevance of documents. It uses models from the XGBoost and RankLib libraries. These models rescore the search results, consideri...

Module : L<OpenSearch::Client::Core::3_0::Direct::LTR>

=head2 ml

OpenSearch supports ML models that you can use to enhance search relevance through semantic understanding. You can either deploy models directly within your OpenSearch cluster or connect to models hosted on external platforms. These models can transf...

Module : L<OpenSearch::Client::Core::3_0::Direct::ML>

=head2 neural

The Neural Search plugin provides several APIs for monitoring semantic and hybrid search features.

Module : L<OpenSearch::Client::Core::3_0::Direct::Neural>

=head2 nodes

The Nodes API makes it possible to retrieve information about individual nodes in your cluster.

Module : L<OpenSearch::Client::Core::3_0::Direct::Nodes>

=head2 notifications

The Notifications plugin provides a central location for all of your notifications from OpenSearch plugins. Using the plugin, you can configure which communication service you want to use and see relevant statistics and troubleshooting information. C...

Module : L<OpenSearch::Client::Core::3_0::Direct::Notifications>

=head2 observability

OpenSearch provides observability capabilities for monitoring applications, infrastructure, and AI agents.