Add a descriptor set and properties associated with it.


  • name: Name of the DescriptorSet.
  • dimensions: Number of dimensions of the feature vector.
  • [optional] engine: Specifies the implementation for indexing and computing distances.
  • [optional] metric: Specifies the metric used to calculate distances.



A DescriptorSet is a group of descriptors with a fixed number of dimensions that are the result of the same algorithm for feature extraction. For instance, we can create a DescriptorSet and insert multiple descriptors obtained by using OpenFace (128 dimensions), and then index and perform matching operations over those descriptors.

A DescriptorSet is conditionally added if and only if a DescriptorSet with the same name does not exist.

The engine parameter specifies the method used to index descriptors, and consequently, the structure of the index. The engine defines trade-offs of accuracy vs latency: some indexes will compute “exact” search, taking longer to compute, and some other indexes will compute an “approximate” search much faster. The supported engines are:

  • FaissFlat [Exact search] [Default]
  • FaissIVFFlat [Approximate search]
  • TileDBDense [Exact search]
  • TileDBSparse [Exact search]

The metric parameter specifies the metric used to compute distances between descriptors, when performing similarity search. The supported metrics are:

  • L2 (euclidean distance) [Default]
  • IP (inner product)


Insert a DescriptorSet for face recognition, using 128-dimensional descriptors and engine for exact search (FaissFlat).

[ {
    "AddDescriptorSet": {
        "name":       "pretty_faces",
        "dimensions": 128,
        "engine":     "FaissFlat",
        "metric":     "L2",
        "properties": {
            "year_created": 2018,
            "description":  "face recognition descriptors"
} ]