Creating GPU-accelerated vector indexes - Amazon OpenSearch Service
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Creating GPU-accelerated vector indexes

After enabling GPU-acceleration on your domain or collection, create vector indexes that can take advantage of GPU processing.

Note

When you create a domain with GPU-acceleration enabled, the index.knn.remote_index_build.enabled setting is true by default. You don't need to explicitly set this setting when creating indexes. For collections, you must explicitly specify a value for this setting.

Creating index with GPU-acceleration

The following example creates a vector index optimized for GPU processing. This index stores 768-dimensional vectors (common for text embeddings).

PUT my-vector-index
{
  "settings": {
    "index.knn": true,
    "index.knn.remote_index_build.enabled": true
  },
  "mappings": {
    "properties": {
      "vector_field": {
        "type": "knn_vector",
        "dimension": 768
      },
      "text": {
        "type": "text"
      }
    }
  }
}

Key configuration elements:

  • "index.knn": true - Enables k-nearest neighbor functionality

  • "index.knn.remote_index_build.enabled": true - Enables GPU processing for this index. When the domain has GPU-acceleration enabled, not specifying this setting defaults to true. For collections, you must explicitly specify a value for this setting.

  • "dimension": 768 - Specifies vector size (adjust based on your embedding model)

Creating index without GPU-acceleration

The following example creates a vector index where GPU processing is disabled. This index stores 768-dimensional vectors (common for text embeddings).

PUT my-vector-index
{
  "settings": {
    "index.knn": true,
    "index.knn.remote_index_build.enabled": false
  },
  "mappings": {
    "properties": {
      "vector_field": {
        "type": "knn_vector",
        "dimension": 768
      },
      "text": {
        "type": "text"
      }
    }
  }
}