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.

This plugin is used in the blog post about Neural Search with Solr: https://medium.com/@dmitry.kan/neural-search-with-bert-and-solr-ea5ead060b28

The upgrade path from lucene / solr 6.6.0 to 8.x is documented here: https://medium.com/swlh/fun-with-apache-lucene-and-bert-embeddings-c2c496baa559

How you can help

1. Pick a suitable release version for your Solr and test it.
2. Create issues (and patches)
3. Spread the word!

How to release a version

This project is using Maven release plugin.

Using this command you can test the release process:

mvn release:prepare -DdryRun=true

If you think it is good to go, issue:

mvn release:prepare

Plugin installation

The plugin was developed and tested on Solr 6.6.0.

1. Copy VectorPlugin.jar to {solr.install.dir}/dist/plugins/
2. Add the library to solrconfig.xml file:

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

3. Add the plugin Query parser to solrconfig.xml:

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

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

  <fieldType name="VectorField" class="solr.TextField" indexed="true" termOffsets="true" stored="true" termPayloads="true" termPositions="true" termVectors="true" storeOffsetsWithPositions="true">
    <analyzer>
      <tokenizer class="solr.WhitespaceTokenizerFactory"/>
      <filter class="solr.DelimitedPayloadTokenFilterFactory" encoder="float"/>
    </analyzer>
  </fieldType>

5. Add the field vector to schema file:

<field name="vector" type="VectorField" indexed="true" termOffsets="true" stored="true" termPositions="true" termVectors="true" multiValued="true"/>

6. Start Solr!

Example

Add example documents

curl -X POST -H "Content-Type: application/json" http://localhost:8983/solr/{your-collection-name}/update?commit=true  --data-binary '
[
    {"name":"example 0", "vector":"0|1.55 1|3.53 2|2.3 3|0.7 4|3.44 5|2.33 "},
    {"name":"example 1", "vector":"0|3.54 1|0.4 2|4.16 3|4.88 4|4.28 5|4.25 "},
    {"name":"example 2", "vector":"0|1.11 1|0.6 2|1.47 3|1.99 4|2.91 5|1.01 "},
    {"name":"example 3", "vector":"0|0.06 1|4.73 2|0.29 3|1.27 4|0.69 5|3.9 "},
    {"name":"example 4", "vector":"0|4.01 1|3.69 2|2 3|4.36 4|1.09 5|0.1 "},
    {"name":"example 5", "vector":"0|0.64 1|3.95 2|1.03 3|1.65 4|0.99 5|0.09 "}
]'

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="0.1,4.75,0.3,1.2,0.7,4.0"}

You should see the following result:

{
  "responseHeader":{
    "status":0,
    "QTime":1,
    "params":{
      "q":"{!myqp f=vector vector=\"0.1,4.75,0.3,1.2,0.7,4.0\"}",
      "fl":"name,score,vector"}},
  "response":{"numFound":6,"start":0,"maxScore":0.99984086,"docs":[
      {
        "name":["example 3"],
        "vector":["0|0.06 1|4.73 2|0.29 3|1.27 4|0.69 5|3.9 "],
        "score":0.99984086},
      {
        "name":["example 0"],
        "vector":["0|1.55 1|3.53 2|2.3 3|0.7 4|3.44 5|2.33 "],
        "score":0.7693964},
      {
        "name":["example 5"],
        "vector":["0|0.64 1|3.95 2|1.03 3|1.65 4|0.99 5|0.09 "],
        "score":0.76322395},
      {
        "name":["example 4"],
        "vector":["0|4.01 1|3.69 2|2 3|4.36 4|1.09 5|0.1 "],
        "score":0.5328145},
      {
        "name":["example 1"],
        "vector":["0|3.54 1|0.4 2|4.16 3|4.88 4|4.28 5|4.25 "],
        "score":0.48513117},
      {
        "name":["example 2"],
        "vector":["0|1.11 1|0.6 2|1.47 3|1.99 4|2.91 5|1.01 "],
        "score":0.44909418}]
  }}

Query 2

Adding the parameter cosine=false calculates the dot product

http://localhost:8983/solr/{your-collection-name}/query?fl=name,score,vector&q={!vp f=vector vector="0.1,4.75,0.3,1.2,0.7,4.0" cosine=false}

result of query 2:

{
  "responseHeader":{
    "status":0,
    "QTime":0,
    "params":{
      "q":"{!myqp f=vector vector=\"0.1,4.75,0.3,1.2,0.7,4.0\" cosine=false}",
      "fl":"name,score,vector"}},
  "response":{"numFound":6,"start":0,"maxScore":40.1675,"docs":[
      {
        "name":["example 3"],
        "vector":["0|0.06 1|4.73 2|0.29 3|1.27 4|0.69 5|3.9 "],
        "score":40.1675},
      {
        "name":["example 0"],
        "vector":["0|1.55 1|3.53 2|2.3 3|0.7 4|3.44 5|2.33 "],
        "score":30.180502},
      {
        "name":["example 1"],
        "vector":["0|3.54 1|0.4 2|4.16 3|4.88 4|4.28 5|4.25 "],
        "score":29.354},
      {
        "name":["example"],
        "vector":["0|4.01 1|3.69 2|2 3|4.36 4|1.09 5|0.1 "],
        "score":24.923502},
      {
        "name":["example"],
        "vector":["0|0.64 1|3.95 2|1.03 3|1.65 4|0.99 5|0.09 "],
        "score":22.1685},
      {
        "name":["example"],
        "vector":["0|1.11 1|0.6 2|1.47 3|1.99 4|2.91 5|1.01 "],
        "score":11.867001}]
  }}

Query 3

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="0.1,4.75,0.3,1.2,0.7,4.0" cosine=false}name="example 2","example 4"

result of query 3:

{
  "responseHeader":{
    "status":0,
    "QTime":1,
    "params":{
      "q":"{!myqp f=vector vector=\"0.1,4.75,0.3,1.2,0.7,4.0\" cosine=false}name=\"example 2\",\"example 4\"",
      "fl":"name,score,vector"}},
  "response":{"numFound":2,"start":0,"maxScore":24.923502,"docs":[
      {
        "name":["example 4"],
        "vector":["0|4.01 1|3.69 2|2 3|4.36 4|1.09 5|0.1 "],
        "score":24.923502},
      {
        "name":["example 2"],
        "vector":["0|1.11 1|0.6 2|1.47 3|1.99 4|2.91 5|1.01 "],
        "score":11.867001}]
  }}