Nomenclature changes from Whitelist to Allowlist (#534)

Signed-off-by: Naveen Tatikonda <navtat@amazon.com>

Signed-off-by: Naveen Tatikonda <navtat@amazon.com>
(cherry picked from commit c7379f8)

src/main/java/org/opensearch/knn/plugin/script/KNNWhitelistExtension.java → src/main/java/org/opensearch/knn/plugin/script/KNNAllowlistExtension.java

@@ -16,9 +16,9 @@
 import java.util.List;
 import java.util.Map;
 
-public class KNNWhitelistExtension implements PainlessExtension {
+public class KNNAllowlistExtension implements PainlessExtension {
 
-    private static final Whitelist ALLOW_LIST = WhitelistLoader.loadFromResourceFiles(KNNWhitelistExtension.class, "knn_whitelist.txt");
+    private static final Whitelist ALLOW_LIST = WhitelistLoader.loadFromResourceFiles(KNNAllowlistExtension.class, "knn_allowlist.txt");
 
     @Override
     public Map<ScriptContext<?>, List<Whitelist>> getContextWhitelists() {

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

@@ -65,7 +65,7 @@ private static float[] toFloat(List<Number> inputVector) {
     }
 
     /**
-     * Whitelisted l2Squared method for users to calculate L2 squared distance between query vector
+     * Allowlisted l2Squared method for users to calculate L2 squared distance between query vector
      * and document vectors
      * Example
      *  "script": {
@@ -110,7 +110,7 @@ public static float cosinesimilOptimized(float[] queryVector, float[] inputVecto
     }
 
     /**
-     * Whitelisted cosineSimilarity method that can be used in a script to avoid repeated
+     * Allowlisted cosineSimilarity method that can be used in a script to avoid repeated
      * calculation of normalization for the query vector.
      * Example:
      *  "script": {
@@ -156,7 +156,7 @@ public static float cosinesimil(float[] queryVector, float[] inputVector) {
     }
 
     /**
-     * Whitelisted cosineSimilarity method for users to calculate cosine similarity between query vectors and
+     * Allowlisted cosineSimilarity method for users to calculate cosine similarity between query vectors and
      * document vectors
      * Example:
      *  "script": {
@@ -216,7 +216,7 @@ public static float l1Norm(float[] queryVector, float[] inputVector) {
     }
 
     /**
-     * Whitelisted l1distance method for users to calculate L1 distance between query vector
+     * Allowlisted l1distance method for users to calculate L1 distance between query vector
      * and document vectors
      * Example
      *  "script": {
@@ -254,7 +254,7 @@ public static float lInfNorm(float[] queryVector, float[] inputVector) {
     }
 
     /**
-     * Whitelisted lInfNorm method for users to calculate L-inf distance between query vector
+     * Allowlisted lInfNorm method for users to calculate L-inf distance between query vector
      * and document vectors
      * Example
      *  "script": {
@@ -291,7 +291,7 @@ public static float innerProduct(float[] queryVector, float[] inputVector) {
     }
 
     /**
-     * Whitelisted innerProd method for users to calculate inner product distance between query vector
+     * Allowlisted innerProd method for users to calculate inner product distance between query vector
      * and document vectors
      * Example
      *  "script": {

src/main/resources/META-INF/services/org.opensearch.painless.spi.PainlessExtension

@@ -1,4 +1,4 @@
 # Copyright OpenSearch Contributors
 # SPDX-License-Identifier: Apache-2.0
 
-org.opensearch.knn.plugin.script.KNNWhitelistExtension
+org.opensearch.knn.plugin.script.KNNAllowlistExtension

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

@@ -169,7 +169,7 @@ public void testBitHammingDistance_Long() {
         assertEquals(0.0, KNNScoringUtil.calculateHammingBit(long3, long3), 0.1);
     }
 
-    public void testL2SquaredWhitelistedScoringFunction() throws IOException {
+    public void testL2SquaredAllowlistedScoringFunction() throws IOException {
         List<Number> queryVector = getTestQueryVector();
         TestKNNScriptDocValues dataset = new TestKNNScriptDocValues();
         dataset.createKNNVectorDocument(new float[] { 4.0f, 4.0f, 4.0f }, "test-index-field-name");