This functionality is experimental and may be changed or removed completely in a future release. Elastic will take a best effort approach to fix any issues, but experimental features are not subject to the support SLA of official GA features.

A sparse_vector field stores sparse vectors of float values. The maximum number of dimensions that can be in a vector should not exceed 500. The number of dimensions can be different across documents. A sparse_vector field is a single-valued field.

These vectors can be used for document scoring. For example, a document score can represent a distance between a given query vector and the indexed document vector.

You represent a sparse vector as an object, where object fields are dimensions, and fields values are values for these dimensions. Dimensions are integer values from 0 to 65535 encoded as strings. Dimensions don’t need to be in order.

PUT my_index
{
  "mappings": {
    "properties": {
      "my_vector": {
        "type": "sparse_vector"
      },
      "my_text" : {
        "type" : "keyword"
      }
    }
  }
}

PUT my_index/_doc/1
{
  "my_text" : "text1",
  "my_vector" : {"1": 0.5, "5": -0.5,  "100": 1}
}

PUT my_index/_doc/2
{
  "my_text" : "text2",
  "my_vector" : {"103": 0.5, "4": -0.5,  "5": 1, "11" : 1.2}
}

Internally, each document’s sparse vector is encoded as a binary doc value. Its size in bytes is equal to 6 * NUMBER_OF_DIMENSIONS, where NUMBER_OF_DIMENSIONS - number of the vector’s dimensions.