jelmerk / elasticsearch-vector-scoring

Fast Elasticsearch Vector Scoring

This Plugin allows you to score Elasticsearch documents based on embedding-vectors, using dot-product or cosine-similarity.

General

• Updated version for ES 6.1 of this plugin
• Cosine support removed.
• This plugin was inspired from This elasticsearch vector scoring plugin and this discussion to achieve 10 times faster processing over the original.
• lior-k gained this substantial speed improvement by using the lucene index directly
• lior-k developed it for their workplace which needs to pick KNN from a set of ~4M vectors. Their current ES setup is able to answer this in ~80ms

Elasticsearch version

• Currently designed for Elasticsearch 6.1.2.

Setup

In order to install this plugin, you need to create a zip distribution first by running

gradle clean assemble

This will produce a zip file in build/distributions.

After building the zip file, you can install it like this

elasticsearch-plugin install file:///path/to/iplugin/build/distribution/FILENAME.zip

Debugging

Place this into an elasticsearch checkout, add the plugin to the projects list in /settings.gradle and run

gradle :plugins:vector-scoring:run --debug-jvm

Usage

Documents

• Each document you score should have a field containing the base64 representation of your vector. for example:

   {
   	"id": 1,
   	....
   	"content_vector": "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"
   }

• Use this field mapping:

PUT my_index
{
  "mappings": {
    "doc": {
      "properties": {
        "embedding_vector": {
          "type": "binary",
          "doc_values": true
        }
      }
    }
  }
}

• The vector can be of any dimension

Querying

• For querying the 100 KNN documents use this POST message on your ES index:

POST /_search
{
  "query": {
    "function_score": {
      "query": {
        "match": {
          "name": "Doe"
        }
      },
      "functions": [
        {
          "script_score": {
            "script": {
              "source": "vector_scoring",
              "lang": "binary_vector_score",
              "params": {
                "vector_field": "content_vector",
                "vector": [
                  -0.09217305481433868,
                  0.010635560378432274,
                  -0.02878434956073761,
                  0.06988169997930527,
                  0.1273992955684662,
                  -0.023723633959889412,
                  0.05490724742412567,
                  -0.12124507874250412,
                  -0.023694118484854698,
                  0.014595639891922474,
                  0.1471538096666336,
                  0.044936809688806534,
                  -0.02795785665512085,
                  -0.05665992572903633,
                  -0.2441125512123108,
                  0.2755320072174072,
                  0.11451690644025803,
                  0.20242854952812195,
                  -0.1387604922056198,
                  0.05219579488039017,
                  0.1145530641078949,
                  0.09967200458049774,
                  0.2161576747894287,
                  0.06157230958342552,
                  0.10350126028060913,
                  0.20387393236160278,
                  0.1367097795009613,
                  0.02070528082549572,
                  0.19238869845867157,
                  0.059613026678562164,
                  0.014012521132826805,
                  0.16701748967170715,
                  0.04985826835036278,
                  -0.10990987718105316,
                  -0.12032567709684372,
                  -0.1450948715209961,
                  0.13585780560970306,
                  0.037511035799980164,
                  0.04251480475068092,
                  0.10693439096212387,
                  -0.08861573040485382,
                  -0.07457160204648972,
                  0.0549330934882164,
                  0.19136285781860352,
                  0.03346432000398636,
                  -0.03652812913060188,
                  -0.1902569830417633,
                  0.03250952064990997,
                  -0.3061246871948242,
                  0.05219300463795662,
                  -0.07879918068647385,
                  0.1403723508119583,
                  -0.08893408626317978,
                  -0.24330253899097443,
                  -0.07105310261249542,
                  -0.18161986768245697,
                  0.15501035749912262,
                  -0.216160386800766,
                  -0.06377710402011871,
                  -0.07671763002872467,
                  0.05360138416290283,
                  -0.052845533937215805,
                  -0.02905619889497757,
                  0.08279753476381302
                ]
              }
            }
          }
        }
      ]
    }
  }
}

• The example above shows a vector of 64 dimensions
• Parameters:
  1. field_vector: The field containing the base64 vector.
  2. vector: The vector (comma separated) to compare to.