Vector Scoring Plugin for Solr : Dot Product and Cosine Similarity

With this plugin you can query documents with vectors and score them based on dot product or cosine similarity. This plugin is the same as Vector Scoring Plugin for Elasticsearch.

Plugin installation

The plugin was developed and tested on Solr 7.4.0.

Build the jar using maven

mvn -Prelease clean package

Copy the jar Vector-Plugin-deploy-*.jar to {solr.install.dir}/dist/plugins/
Add the library to solrconfig.xml file:

<lib dir="${solr.install.dir:../../../..}/dist/plugins/" regex=".*\.jar" />

Add the plugin Query parser to solrconfig.xml:

<queryParser name="vp" class="com.github.saaay71.solr.query.VectorQParserPlugin" />

Add the fieldType VectorField to schema file(managed-schema):

      <fieldType name="VectorField" class="solr.BinaryField" stored="true" indexed="false" multiValued="false"/>

Add the field vector to schema file:

    <field name="_vector_" type="VectorField" />
    <field name="_lsh_hash_" type="string" indexed="true" stored="true" multiValued="true"/>
    <field name="vector" type="string" indexed="true" stored="true"/>

Add the LSH urp to solrconfig.xml

    <updateRequestProcessorChain name="LSH">
        <processor class="com.github.saaay71.solr.updateprocessor.LSHUpdateProcessorFactory" >
            <int name="seed">5</int>
            <int name="buckets">50</int>
            <int name="stages">50</int>
            <int name="dimensions">6</int>
            <str name="field">vector</str>
        </processor>
        <processor class="solr.RunUpdateProcessorFactory" />
    </updateRequestProcessorChain>

Start Solr!

Example

Add example documents

curl -X POST -H "Content-Type: application/json" http://localhost:8983/solr/{your-collection-name}/update?update.chain=LSH&commit=true  --data-binary '
[
    {"id":"1", "vector":"1.55,3.53,2.3,0.7,3.44,2.33"},
    {"id":"2", "vector":"3.54,0.4,4.16,4.88,4.28,4.25"}
]'

Query documents

Open your browser and copy the links

Query 1

http://localhost:8983/solr/{your-collection-name}/query?fl=name,score,vector&q={!vp f=vector vector=\"1.55,3.53,2.3,0.7,3.44,2.33\" lsh=\"true\" reRankDocs=\"5\"}&fl=name,score,vector,_vector_,_lsh_hash_

You should see the following result:

{
  "responseHeader":{
    "status":0,
    "QTime":8,
    "params":{
      "q":"{!vp f=vector vector=\"1.55,3.53,2.3,0.7,3.44,2.33\" lsh=\"true\" reRankDocs=\"5\"}",
      "fl":"id, score, vector, _vector_, _lsh_hash_",
      "wt":"xml"}},
  "response":{"numFound":1,"start":0,"maxScore":36.65736,"docs":[
      {
        "id": "1",
        "vector":"1.55,3.53,2.3,0.7,3.44,2.33",
        "_vector_":"/z/GZmZAYeuFQBMzMz8zMzNAXCj2QBUeuA==",
        "_lsh_hash_":["0_8",
          "1_35",
          "2_7",
          "3_10",
          "4_2",
          "5_35",
          "6_16",
          "7_30",
          "8_27",
          "9_12",
          "10_7",
          "11_32",
          "12_48",
          "13_36",
          "14_10",
          "15_7",
          "16_42",
          "17_5",
          "18_3",
          "19_2",
          "20_1",
          "21_0",
          "22_24",
          "23_18",
          "24_42",
          "25_31",
          "26_35",
          "27_8",
          "28_1",
          "29_24",
          "30_47",
          "31_14",
          "32_22",
          "33_39",
          "34_0",
          "35_34",
          "36_34",
          "37_39",
          "38_27",
          "39_27",
          "40_45",
          "41_10",
          "42_21",
          "43_34",
          "44_41",
          "45_9",
          "46_31",
          "47_0",
          "48_4",
          "49_43"],
        "score":36.65736}
      ]
  }
}

Query 2

Quering on other fields and with vector scoring.

http://localhost:8983/solr/{your-collection-name}/query?fl=name,score,vector&q={!vp f=vector vector=\"3.54,0.4,4.16,4.88,4.28,4.25\" lsh=\"true\" reRankDocs=\"5\" v=\"id:2\"}&fl=name,score,vector,_vector_,_lsh_hash_

http://localhost:8983/solr/{your-collection-name}/query?fl=name,score,vector&q={!vp f=vector vector=\"3.54,0.4,4.16,4.88,4.28,4.25\" lsh=\"true\" reRankDocs=\"5\"} id:2&fl=name,score,vector,_vector_,_lsh_hash_

result of query 2:

{
  "responseHeader":{
    "status":0,
    "QTime":9,
    "params":{
      "q":"{!vp f=vector vector=\"3.54,0.4,4.16,4.88,4.28,4.25\" lsh=\"true\" reRankDocs=\"5\" v=\"id:2\"}",
      "fl":"name, score, vector, _vector_, _lsh_hash_",
      "wt":"xml"}},
  "response":{"numFound":1,"start":0,"maxScore":38.649788,"docs":[
      {
        "id": "2",
        "vector":"3.54,0.4,4.16,4.88,4.28,4.25",
        "_vector_":"/0Bij1w+zMzNQIUeuECcKPZAiPXDQIgAAA==",
        "_lsh_hash_":["0_2",
          "1_33",
          "2_39",
          "3_49",
          "4_38",
          "5_12",
          "6_21",
          "7_26",
          "8_44",
          "9_25",
          "10_15",
          "11_25",
          "12_24",
          "13_8",
          "14_22",
          "15_43",
          "16_1",
          "17_17",
          "18_14",
          "19_1",
          "20_26",
          "21_47",
          "22_15",
          "23_36",
          "24_21",
          "25_41",
          "26_32",
          "27_35",
          "28_13",
          "29_4",
          "30_2",
          "31_39",
          "32_19",
          "33_36",
          "34_15",
          "35_30",
          "36_17",
          "37_0",
          "38_39",
          "39_32",
          "40_5",
          "41_1",
          "42_33",
          "43_0",
          "44_32",
          "45_21",
          "46_23",
          "47_4",
          "48_24",
          "49_16"],
        "score":38.649788}]
  }
}