Use the Lucene Distance Calculation Function in Script Scoring for doing exact search

CHANGELOG.md

@@ -14,6 +14,7 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 
 ## [Unreleased 2.x](https://github.com/opensearch-project/k-NN/compare/2.14...2.x)
 ### Features
+* Use the Lucene Distance Calculation Function in Script Scoring for doing exact search [#1699](https://github.com/opensearch-project/k-NN/pull/1699)
 ### Enhancements
 * Add KnnCircuitBreakerException and modify exception message [#1688](https://github.com/opensearch-project/k-NN/pull/1688)
 ### Bug Fixes

src/main/java/org/opensearch/knn/plugin/script/KNNScoringUtil.java

@@ -10,6 +10,7 @@
 import java.util.Objects;
 import org.apache.logging.log4j.LogManager;
 import org.apache.logging.log4j.Logger;
+import org.apache.lucene.util.VectorUtil;
 import org.opensearch.knn.index.KNNVectorScriptDocValues;
 import org.opensearch.knn.index.SpaceType;
 import org.opensearch.knn.index.VectorDataType;
@@ -48,13 +49,7 @@ private static void requireEqualDimension(final float[] queryVector, final float
      * @return L2 score
      */
     public static float l2Squared(float[] queryVector, float[] inputVector) {
-        requireEqualDimension(queryVector, inputVector);
-        float squaredDistance = 0;
-        for (int i = 0; i < inputVector.length; i++) {
-            float diff = queryVector[i] - inputVector[i];
-            squaredDistance += diff * diff;
-        }
-        return squaredDistance;
+        return VectorUtil.squareDistance(queryVector, inputVector);
     }
 
     private static float[] toFloat(List<Number> inputVector, VectorDataType vectorDataType) {
@@ -148,20 +143,12 @@ public static float cosineSimilarity(List<Number> queryVector, KNNVectorScriptDo
      */
     public static float cosinesimil(float[] queryVector, float[] inputVector) {
         requireEqualDimension(queryVector, inputVector);
-        float dotProduct = 0.0f;
-        float normQueryVector = 0.0f;
-        float normInputVector = 0.0f;
-        for (int i = 0; i < queryVector.length; i++) {
-            dotProduct += queryVector[i] * inputVector[i];
-            normQueryVector += queryVector[i] * queryVector[i];
-            normInputVector += inputVector[i] * inputVector[i];
-        }
-        float normalizedProduct = normQueryVector * normInputVector;
-        if (normalizedProduct == 0) {
+        try {
+            return VectorUtil.cosine(queryVector, inputVector);
+        } catch (IllegalArgumentException | AssertionError e) {
             logger.debug("Invalid vectors for cosine. Returning minimum score to put this result to end");
             return 0.0f;
         }
-        return (float) (dotProduct / (Math.sqrt(normalizedProduct)));
     }
 
     /**
@@ -217,7 +204,6 @@ public static float calculateHammingBit(Long queryLong, Long inputLong) {
      * @return L1 score
      */
     public static float l1Norm(float[] queryVector, float[] inputVector) {
-        requireEqualDimension(queryVector, inputVector);
         float distance = 0;
         for (int i = 0; i < inputVector.length; i++) {
             float diff = queryVector[i] - inputVector[i];
@@ -255,7 +241,6 @@ public static float l1Norm(List<Number> queryVector, KNNVectorScriptDocValues do
      * @return L-inf score
      */
     public static float lInfNorm(float[] queryVector, float[] inputVector) {
-        requireEqualDimension(queryVector, inputVector);
         float distance = 0;
         for (int i = 0; i < inputVector.length; i++) {
             float diff = queryVector[i] - inputVector[i];
@@ -293,12 +278,7 @@ public static float lInfNorm(List<Number> queryVector, KNNVectorScriptDocValues
      * @return dot product score
      */
     public static float innerProduct(float[] queryVector, float[] inputVector) {
-        requireEqualDimension(queryVector, inputVector);
-        float distance = 0;
-        for (int i = 0; i < inputVector.length; i++) {
-            distance += queryVector[i] * inputVector[i];
-        }
-        return distance;
+        return VectorUtil.dotProduct(queryVector, inputVector);
     }
 
     /**

src/test/java/org/opensearch/knn/plugin/script/KNNScoringSpaceTests.java

@@ -47,7 +47,7 @@ public void testL2() {
 
     public void testCosineSimilarity() {
         float[] arrayFloat = new float[] { 1.0f, 2.0f, 3.0f };
-        List<Double> arrayListQueryObject = new ArrayList<>(Arrays.asList(1.0, 2.0, 3.0));
+        List<Double> arrayListQueryObject = new ArrayList<>(Arrays.asList(2.0, 4.0, 6.0));
         float[] arrayFloat2 = new float[] { 2.0f, 4.0f, 6.0f };
         KNNMethodContext knnMethodContext = KNNMethodContext.getDefault();
 
@@ -59,7 +59,7 @@ public void testCosineSimilarity() {
         );
         KNNScoringSpace.CosineSimilarity cosineSimilarity = new KNNScoringSpace.CosineSimilarity(arrayListQueryObject, fieldType);
 
-        assertEquals(3F, cosineSimilarity.scoringMethod.apply(arrayFloat2, arrayFloat), 0.1F);
+        assertEquals(2F, cosineSimilarity.scoringMethod.apply(arrayFloat2, arrayFloat), 0.1F);
 
         // invalid zero vector
         final List<Float> queryZeroVector = List.of(0.0f, 0.0f, 0.0f);