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Painless scripting extensions

With Painless scripting extensions, you can use k-nearest neighbors (k-NN) distance functions directly in your Painless scripts to perform operations on knn_vector fields. Painless has a strict list of allowed functions and classes per context to ensure that its scripts are secure. OpenSearch adds Painless scripting extensions to a few of the distance functions used in k-NN scoring script, so you can use them to customize your k-NN workload.

Get started with k-NN Painless scripting functions

To use k-NN Painless scripting functions, first create an index with knn_vector fields, as described in Getting started with the scoring script for vectors. Once you have created the index and ingested some data, you can use Painless extensions:

GET my-knn-index-2/_search
{
  "size": 2,
  "query": {
    "script_score": {
      "query": {
        "bool": {
          "filter": {
            "term": {
              "color": "BLUE"
            }
          }
        }
      },
      "script": {
        "source": "1.0 + cosineSimilarity(params.query_value, doc[params.field])",
        "params": {
          "field": "my_vector",
          "query_value": [9.9, 9.9]
        }
      }
    }
  }
}

field needs to map to a knn_vector field, and query_value must be a floating-point array with the same dimension as field.

Function types

The following table describes the Painless functions OpenSearch provides.

Function name Function signature Description
l2Squared float l2Squared (float[] queryVector, doc['vector field']) This function calculates the square of the L2 distance (Euclidean distance) between a given query vector and document vectors. A shorter distance indicates a more relevant document, so this example inverts the return value of the l2Squared function. If the document vector matches the query vector, the result is 0, so this example also adds 1 to the distance to avoid divide-by-zero errors.
l1Norm float l1Norm (float[] queryVector, doc['vector field']) This function calculates the L1 norm distance (Manhattan distance) between a given query vector and document vectors.
cosineSimilarity float cosineSimilarity (float[] queryVector, doc['vector field']) Cosine similarity is an inner product of the query vector and document vector normalized to both have a length of 1. If the magnitude of the query vector doesn’t change throughout the query, you can pass the magnitude of the query vector to improve performance instead of repeatedly calculating the magnitude for every filtered document:
float cosineSimilarity (float[] queryVector, doc['vector field'], float normQueryVector)
In general, the range of cosine similarity is [-1, 1]. However, in the case of information retrieval, the cosine similarity of two documents ranges from 0 to 1 because the tf-idf statistic can’t be negative. Therefore, OpenSearch adds 1.0 in order to always yield a positive cosine similarity score.
hamming float hamming (float[] queryVector, doc['vector field']) This function calculates the Hamming distance between a given query vector and document vectors. The Hamming distance is the number of positions at which the corresponding elements are different. A shorter distance indicates a more relevant document, so this example inverts the return value of the Hamming distance.

The hamming space type is supported for binary vectors in OpenSearch version 2.16 and later. For more information, see Binary k-NN vectors.

Constraints

  1. If a document’s knn_vector field has different dimensions than the query, the function throws an IllegalArgumentException.

  2. If a vector field doesn’t have a value, the function throws an IllegalStateException.

    You can avoid this by first checking whether a document contains a value in its field:

    "source": "doc[params.field].size() == 0 ? 0 : 1 / (1 + l2Squared(params.query_value, doc[params.field]))",

    Because scores can only be positive, this script ranks documents with vector fields higher than those without vector fields.

When using cosine similarity, it is not valid to pass a zero vector ([0, 0, ...]) as input. This is because the magnitude of such a vector is 0, which raises a divide by 0 exception in the corresponding formula. Requests containing the zero vector will be rejected, and a corresponding exception will be thrown.

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