Replace some NeuralSparseIT test case's model inference with fixed qu…

…eryToken, add some comments, replace some String with String.format.

Signed-off-by: conggguan <[email protected]>

src/main/java/org/opensearch/neuralsearch/query/NeuralSparseQuery.java

@@ -38,14 +38,12 @@ public final class NeuralSparseQuery extends Query {
      */
     @Override
     public String toString(String field) {
-        return "NeuralSparseQuery("
-            + currentQuery.toString(field)
-            + ","
-            + highScoreTokenQuery.toString(field)
-            + ", "
-            + lowScoreTokenQuery.toString(field)
-            + ")";
-
+        return String.format(
+            "NeuralSparseQuery(%s,%s,%s)",
+            currentQuery.toString(field),
+            highScoreTokenQuery.toString(field),
+            lowScoreTokenQuery.toString(field)
+        );
     }
 
     public Query rewrite(IndexSearcher indexSearcher) throws IOException {

src/main/java/org/opensearch/neuralsearch/query/NeuralSparseQueryBuilder.java

@@ -412,13 +412,14 @@ protected boolean doEquals(NeuralSparseQueryBuilder obj) {
 
     @Override
     protected int doHashCode() {
-        HashCodeBuilder builder = new HashCodeBuilder().append(fieldName).append(queryText).append(modelId).append(maxTokenScore);
+        HashCodeBuilder builder = new HashCodeBuilder().append(fieldName)
+            .append(queryText)
+            .append(modelId)
+            .append(maxTokenScore)
+            .append(neuralSparseTwoPhaseParameters);
         if (queryTokensSupplier != null) {
             builder.append(queryTokensSupplier.get());
         }
-        if (Objects.nonNull(neuralSparseTwoPhaseParameters)) {
-            builder.append(neuralSparseTwoPhaseParameters.hashcode());
-        }
         return builder.toHashCode();
     }
 
@@ -455,6 +456,9 @@ private Map<String, Float> getFilteredScoreTokens(Map<String, Float> queryTokens
             .stream()
             .filter(entry -> (aboveThreshold == (entry.getValue() >= threshold)))
             .collect(Collectors.toMap(Map.Entry::getKey, Map.Entry::getValue));
+        // This call will generate a filter from queryTokens.
+        // When aboveThreshold is true, will filter out all key-value pairs whose value >= threshold to a return map.
+        // When aboveThreshold is false, will filter out all key-value pairs whose value <= threshold to a return map.
     }
 
     private BooleanQuery buildFeatureFieldQueryFromTokens(Map<String, Float> tokens, String fieldName) {

src/main/java/org/opensearch/neuralsearch/query/NeuralSparseTwoPhaseParameters.java

@@ -63,7 +63,7 @@ public class NeuralSparseTwoPhaseParameters implements Writeable {
      * @param clusterService The opensearch clusterService.
      * @param settings       The env settings to initialize.
      */
-    public static void initialize(ClusterService clusterService, Settings settings) {
+    public static void initialize(final ClusterService clusterService, final Settings settings) {
         DEFAULT_ENABLED = NeuralSearchSettings.NEURAL_SPARSE_TWO_PHASE_DEFAULT_ENABLED.get(settings);
         DEFAULT_WINDOW_SIZE_EXPANSION = NeuralSearchSettings.NEURAL_SPARSE_TWO_PHASE_DEFAULT_WINDOW_SIZE_EXPANSION.get(settings);
         DEFAULT_PRUNING_RATIO = NeuralSearchSettings.NEURAL_SPARSE_TWO_PHASE_DEFAULT_PRUNING_RATIO.get(settings);
@@ -96,14 +96,14 @@ public static NeuralSparseTwoPhaseParameters getDefaultSettings() {
      * @param in StreamInput to initialize object from
      * @throws IOException thrown if unable to read from input stream
      */
-    public NeuralSparseTwoPhaseParameters(StreamInput in) throws IOException {
+    public NeuralSparseTwoPhaseParameters(final StreamInput in) throws IOException {
         window_size_expansion = in.readFloat();
         pruning_ratio = in.readFloat();
         enabled = in.readBoolean();
     }
 
     @Override
-    public void writeTo(StreamOutput out) throws IOException {
+    public void writeTo(final StreamOutput out) throws IOException {
         out.writeFloat(window_size_expansion);
         out.writeFloat(pruning_ratio);
         out.writeBoolean(enabled);
@@ -116,7 +116,7 @@ public void writeTo(StreamOutput out) throws IOException {
      * @return the given XContentBuilder with object content added.
      * @throws IOException if building the content fails.
      */
-    public XContentBuilder doXContent(XContentBuilder builder) throws IOException {
+    public XContentBuilder doXContent(final XContentBuilder builder) throws IOException {
         builder.startObject(NAME.getPreferredName());
         builder.field(WINDOW_SIZE_EXPANSION.getPreferredName(), window_size_expansion);
         builder.field(PRUNING_RATIO.getPreferredName(), pruning_ratio);
@@ -132,7 +132,7 @@ public XContentBuilder doXContent(XContentBuilder builder) throws IOException {
      * @return a new instance of NeuralSparseTwoPhaseParameters initialized from the parser.
      * @throws IOException if parsing fails.
      */
-    public static NeuralSparseTwoPhaseParameters parseFromXContent(XContentParser parser) throws IOException {
+    public static NeuralSparseTwoPhaseParameters parseFromXContent(final XContentParser parser) throws IOException {
         XContentParser.Token token;
         String currentFieldName = "";
         NeuralSparseTwoPhaseParameters neuralSparseTwoPhaseParameters = NeuralSparseTwoPhaseParameters.getDefaultSettings();
@@ -177,7 +177,8 @@ public boolean equals(Object obj) {
             && Objects.equals(pruning_ratio, other.pruning_ratio);
     }
 
-    public int hashcode() {
+    @Override
+    public int hashCode() {
         HashCodeBuilder builder = new HashCodeBuilder().append(enabled).append(window_size_expansion).append(pruning_ratio);
         return builder.toHashCode();
     }
@@ -188,7 +189,7 @@ public int hashcode() {
      * @param neuralSparseTwoPhaseParameters The parameters to check.
      * @return true if enabled, false otherwise.
      */
-    public static boolean isEnabled(NeuralSparseTwoPhaseParameters neuralSparseTwoPhaseParameters) {
+    public static boolean isEnabled(final NeuralSparseTwoPhaseParameters neuralSparseTwoPhaseParameters) {
         if (!isClusterOnOrAfterMinReqVersionForTwoPhaseSearchSupport() || Objects.isNull(neuralSparseTwoPhaseParameters)) {
             return false;
         }

src/main/java/org/opensearch/neuralsearch/search/util/NeuralSparseTwoPhaseUtil.java

@@ -26,31 +26,36 @@
  */
 public class NeuralSparseTwoPhaseUtil {
     /**
+     * This function determine any neuralSparseQuery from query, extract lowScoreTokenQuery from each of them,
+     * And as that, the neuralSparseQuery be extracted 's currentQuery will be changed from allScoreTokenQuery to highScoreTokenQuery.
+     * Then build a QueryRescoreContext of these extra lowScoreTokenQuery and add the built QueryRescoreContext to the searchContext.
+     * Finally, the score of TopDocs will be sum of highScoreTokenQuery and lowScoreTokenQuery, which equals to allTokenQuery.
      * @param query         The whole query include neuralSparseQuery to executed.
      * @param searchContext The searchContext with this query.
      */
-    public static void addRescoreContextFromNeuralSparseQuery(final Query query, SearchContext searchContext) {
+    public static void addRescoreContextFromNeuralSparseQuery(final Query query, final SearchContext searchContext) {
         Map<Query, Float> query2weight = new HashMap<>();
         float windowSizeExpansion = populateQueryWeightsMapAndGetWindowSizeExpansion(query, query2weight, 1.0f, 1.0f);
         Query twoPhaseQuery;
         if (query2weight.isEmpty()) {
             return;
-        } else if (query2weight.size() == 1) {
-            Map.Entry<Query, Float> entry = query2weight.entrySet().stream().findFirst().get();
+        }
+        if (query2weight.size() == 1) {
+            Map.Entry<Query, Float> entry = query2weight.entrySet().iterator().next();
             twoPhaseQuery = new BoostQuery(entry.getKey(), entry.getValue());
         } else {
             twoPhaseQuery = getNestedTwoPhaseQuery(query2weight);
         }
         int curWindowSize = (int) (searchContext.size() * windowSizeExpansion);
         if (curWindowSize < 0 || curWindowSize > NeuralSparseTwoPhaseParameters.MAX_WINDOW_SIZE) {
             throw new IllegalArgumentException(
-                "Two phase final windowSize "
-                    + curWindowSize
-                    + " out of score with limit "
-                    + NeuralSparseTwoPhaseParameters.MAX_WINDOW_SIZE
-                    + "."
-                    + "You can change the value of cluster setting "
-                    + "[plugins.neural_search.neural_sparse.two_phase.max_window_size] to a integer at least 50."
+                String.format(
+                    "Two phase final windowSize %d out of score with limit %d. "
+                        + "You can change the value of cluster setting [plugins.neural_search.neural_sparse.two_phase.max_window_size] "
+                        + "to a integer at least 50.",
+                    curWindowSize,
+                    NeuralSparseTwoPhaseParameters.MAX_WINDOW_SIZE
+                )
             );
         }
         QueryRescorer.QueryRescoreContext rescoreContext = new QueryRescorer.QueryRescoreContext(curWindowSize);