Sparse vector datatypeedit
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 1024. The number of dimensions can be
different across documents. A sparse_vector
field is
a singlevalued 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 + 4
, where NUMBER_OF_DIMENSIONS

number of the vector’s dimensions.