Add binary index support for Lucene engine

Signed-off-by: Jay Deng <jayd0104@gmail.com>

CHANGELOG.md

@@ -16,6 +16,7 @@ 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/2.18...2.x)
 ### Features
+- Add binary index support for Lucene engine. (#2292)[https://github.com/opensearch-project/k-NN/pull/2292]
 ### Enhancements
 - Introduced a writing layer in native engines where relies on the writing interface to process IO. (#2241)[https://github.com/opensearch-project/k-NN/pull/2241]
 ### Bug Fixes

src/main/java/org/opensearch/knn/index/KNNVectorSimilarityFunction.java

@@ -29,7 +29,8 @@ public float compare(byte[] v1, byte[] v2) {
 
         @Override
         public VectorSimilarityFunction getVectorSimilarityFunction() {
-            throw new IllegalStateException("VectorSimilarityFunction is not available for Hamming space");
+            // This is not used in binary case
+            return VectorSimilarityFunction.EUCLIDEAN;
         }
     };
 

src/main/java/org/opensearch/knn/index/VectorDataType.java

@@ -40,7 +40,7 @@ public enum VectorDataType {
 
         @Override
         public FieldType createKnnVectorFieldType(int dimension, VectorSimilarityFunction vectorSimilarityFunction) {
-            throw new IllegalStateException("Unsupported method");
+            return KnnByteVectorField.createFieldType(dimension / 8, vectorSimilarityFunction);
         }
 
         @Override

src/main/java/org/opensearch/knn/index/codec/BasePerFieldKnnVectorsFormat.java

@@ -114,7 +114,12 @@ public KnnVectorsFormat getKnnVectorsFormatForField(final String field) {
                 }
             }
 
-            KNNVectorsFormatParams knnVectorsFormatParams = new KNNVectorsFormatParams(params, defaultMaxConnections, defaultBeamWidth);
+            KNNVectorsFormatParams knnVectorsFormatParams = new KNNVectorsFormatParams(
+                params,
+                defaultMaxConnections,
+                defaultBeamWidth,
+                knnMethodContext.getSpaceType()
+            );
             log.debug(
                 "Initialize KNN vector format for field [{}] with params [{}] = \"{}\" and [{}] = \"{}\"",
                 field,

src/main/java/org/opensearch/knn/index/codec/KNN990Codec/KNN990BinaryVectorScorer.java

@@ -0,0 +1,107 @@
+/*
+ * Copyright OpenSearch Contributors
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+package org.opensearch.knn.index.codec.KNN990Codec;
+
+import org.apache.lucene.codecs.hnsw.FlatVectorsScorer;
+import org.apache.lucene.index.VectorSimilarityFunction;
+import org.apache.lucene.util.Bits;
+import org.apache.lucene.util.VectorUtil;
+import org.apache.lucene.util.hnsw.RandomAccessVectorValues;
+import org.apache.lucene.util.hnsw.RandomVectorScorer;
+import org.apache.lucene.util.hnsw.RandomVectorScorerSupplier;
+
+import java.io.IOException;
+
+public class KNN990BinaryVectorScorer implements FlatVectorsScorer {
+    @Override
+    public RandomVectorScorerSupplier getRandomVectorScorerSupplier(
+        VectorSimilarityFunction vectorSimilarityFunction,
+        RandomAccessVectorValues randomAccessVectorValues
+    ) throws IOException {
+        assert randomAccessVectorValues instanceof RandomAccessVectorValues.Bytes;
+        if (randomAccessVectorValues instanceof RandomAccessVectorValues.Bytes) {
+            return new BinaryRandomVectorScorerSupplier((RandomAccessVectorValues.Bytes) randomAccessVectorValues);
+        }
+        throw new IllegalArgumentException("vectorValues must be an instance of RandomAccessVectorValues.Bytes");
+    }
+
+    @Override
+    public RandomVectorScorer getRandomVectorScorer(
+        VectorSimilarityFunction vectorSimilarityFunction,
+        RandomAccessVectorValues randomAccessVectorValues,
+        float[] queryVector
+    ) throws IOException {
+        throw new IllegalArgumentException("binary vectors do not support float[] targets");
+    }
+
+    @Override
+    public RandomVectorScorer getRandomVectorScorer(
+        VectorSimilarityFunction vectorSimilarityFunction,
+        RandomAccessVectorValues randomAccessVectorValues,
+        byte[] queryVector
+    ) throws IOException {
+        assert randomAccessVectorValues instanceof RandomAccessVectorValues.Bytes;
+        if (randomAccessVectorValues instanceof RandomAccessVectorValues.Bytes) {
+            return new BinaryRandomVectorScorer((RandomAccessVectorValues.Bytes) randomAccessVectorValues, queryVector);
+        }
+        throw new IllegalArgumentException("vectorValues must be an instance of RandomAccessVectorValues.Bytes");
+    }
+
+    static class BinaryRandomVectorScorer implements RandomVectorScorer {
+        private final RandomAccessVectorValues.Bytes vectorValues;
+        private final int bitDimensions;
+        private final byte[] queryVector;
+
+        BinaryRandomVectorScorer(RandomAccessVectorValues.Bytes vectorValues, byte[] query) {
+            this.queryVector = query;
+            this.bitDimensions = vectorValues.dimension() * Byte.SIZE;
+            this.vectorValues = vectorValues;
+        }
+
+        @Override
+        public float score(int node) throws IOException {
+            return (bitDimensions - VectorUtil.xorBitCount(queryVector, vectorValues.vectorValue(node))) / (float) bitDimensions;
+        }
+
+        @Override
+        public int maxOrd() {
+            return vectorValues.size();
+        }
+
+        @Override
+        public int ordToDoc(int ord) {
+            return vectorValues.ordToDoc(ord);
+        }
+
+        @Override
+        public Bits getAcceptOrds(Bits acceptDocs) {
+            return vectorValues.getAcceptOrds(acceptDocs);
+        }
+    }
+
+    static class BinaryRandomVectorScorerSupplier implements RandomVectorScorerSupplier {
+        protected final RandomAccessVectorValues.Bytes vectorValues;
+        protected final RandomAccessVectorValues.Bytes vectorValues1;
+        protected final RandomAccessVectorValues.Bytes vectorValues2;
+
+        public BinaryRandomVectorScorerSupplier(RandomAccessVectorValues.Bytes vectorValues) throws IOException {
+            this.vectorValues = vectorValues;
+            this.vectorValues1 = vectorValues.copy();
+            this.vectorValues2 = vectorValues.copy();
+        }
+
+        @Override
+        public RandomVectorScorer scorer(int ord) throws IOException {
+            byte[] queryVector = vectorValues1.vectorValue(ord);
+            return new BinaryRandomVectorScorer(vectorValues2, queryVector);
+        }
+
+        @Override
+        public RandomVectorScorerSupplier copy() throws IOException {
+            return new BinaryRandomVectorScorerSupplier(vectorValues.copy());
+        }
+    }
+}

src/main/java/org/opensearch/knn/index/codec/KNN990Codec/KNN990HnswBinaryVectorsFormat.java

@@ -0,0 +1,97 @@
+/*
+ * Copyright OpenSearch Contributors
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+package org.opensearch.knn.index.codec.KNN990Codec;
+
+import org.apache.lucene.codecs.KnnVectorsFormat;
+import org.apache.lucene.codecs.KnnVectorsReader;
+import org.apache.lucene.codecs.KnnVectorsWriter;
+import org.apache.lucene.codecs.hnsw.FlatVectorsFormat;
+import org.apache.lucene.codecs.lucene99.Lucene99FlatVectorsFormat;
+import org.apache.lucene.codecs.lucene99.Lucene99HnswVectorsReader;
+import org.apache.lucene.codecs.lucene99.Lucene99HnswVectorsWriter;
+import org.apache.lucene.index.SegmentReadState;
+import org.apache.lucene.index.SegmentWriteState;
+import org.apache.lucene.search.TaskExecutor;
+
+import java.io.IOException;
+import java.util.concurrent.ExecutorService;
+
+public class KNN990HnswBinaryVectorsFormat extends KnnVectorsFormat {
+
+    private final int maxConn;
+    private final int beamWidth;
+    private static final FlatVectorsFormat flatVectorsFormat = new Lucene99FlatVectorsFormat(new KNN990BinaryVectorScorer());
+    private final int numMergeWorkers;
+    private final TaskExecutor mergeExec;
+
+    private static final String NAME = "KNN990HnswBinaryVectorsFormat";
+
+    public KNN990HnswBinaryVectorsFormat() {
+        this(16, 100, 1, (ExecutorService) null);
+    }
+
+    public KNN990HnswBinaryVectorsFormat(int maxConn, int beamWidth) {
+        this(maxConn, beamWidth, 1, (ExecutorService) null);
+    }
+
+    public KNN990HnswBinaryVectorsFormat(int maxConn, int beamWidth, int numMergeWorkers, ExecutorService mergeExec) {
+        super(NAME);
+        if (maxConn > 0 && maxConn <= 512) {
+            if (beamWidth > 0 && beamWidth <= 3200) {
+                this.maxConn = maxConn;
+                this.beamWidth = beamWidth;
+                if (numMergeWorkers == 1 && mergeExec != null) {
+                    throw new IllegalArgumentException("No executor service is needed as we'll use single thread to merge");
+                } else {
+                    this.numMergeWorkers = numMergeWorkers;
+                    if (mergeExec != null) {
+                        this.mergeExec = new TaskExecutor(mergeExec);
+                    } else {
+                        this.mergeExec = null;
+                    }
+
+                }
+            } else {
+                throw new IllegalArgumentException("beamWidth must be positive and less than or equal to 3200; beamWidth=" + beamWidth);
+            }
+        } else {
+            throw new IllegalArgumentException("maxConn must be positive and less than or equal to 512; maxConn=" + maxConn);
+        }
+    }
+
+    @Override
+    public KnnVectorsWriter fieldsWriter(SegmentWriteState state) throws IOException {
+        return new Lucene99HnswVectorsWriter(
+            state,
+            this.maxConn,
+            this.beamWidth,
+            flatVectorsFormat.fieldsWriter(state),
+            this.numMergeWorkers,
+            this.mergeExec
+        );
+    }
+
+    @Override
+    public KnnVectorsReader fieldsReader(SegmentReadState state) throws IOException {
+        return new Lucene99HnswVectorsReader(state, flatVectorsFormat.fieldsReader(state));
+    }
+
+    @Override
+    public int getMaxDimensions(String fieldName) {
+        return 1024;
+    }
+
+    @Override
+    public String toString() {
+        return "KNN990HnswBinaryVectorsFormat(name=KNN990HnswBinaryVectorsFormat, maxConn="
+            + this.maxConn
+            + ", beamWidth="
+            + this.beamWidth
+            + ", flatVectorFormat="
+            + flatVectorsFormat
+            + ")";
+    }
+}

src/main/java/org/opensearch/knn/index/codec/KNN990Codec/KNN990PerFieldKnnVectorsFormat.java

@@ -8,6 +8,7 @@
 import org.apache.lucene.codecs.lucene99.Lucene99HnswScalarQuantizedVectorsFormat;
 import org.apache.lucene.codecs.lucene99.Lucene99HnswVectorsFormat;
 import org.opensearch.index.mapper.MapperService;
+import org.opensearch.knn.index.SpaceType;
 import org.opensearch.knn.index.codec.BasePerFieldKnnVectorsFormat;
 import org.opensearch.knn.index.engine.KNNEngine;
 
@@ -24,11 +25,17 @@ public KNN990PerFieldKnnVectorsFormat(final Optional<MapperService> mapperServic
             mapperService,
             Lucene99HnswVectorsFormat.DEFAULT_MAX_CONN,
             Lucene99HnswVectorsFormat.DEFAULT_BEAM_WIDTH,
-            () -> new Lucene99HnswVectorsFormat(),
-            knnVectorsFormatParams -> new Lucene99HnswVectorsFormat(
-                knnVectorsFormatParams.getMaxConnections(),
-                knnVectorsFormatParams.getBeamWidth()
-            ),
+            Lucene99HnswVectorsFormat::new,
+            knnVectorsFormatParams -> {
+                if (knnVectorsFormatParams.getSpaceType() == SpaceType.HAMMING) {
+                    return new KNN990HnswBinaryVectorsFormat(
+                        knnVectorsFormatParams.getMaxConnections(),
+                        knnVectorsFormatParams.getBeamWidth()
+                    );
+                } else {
+                    return new Lucene99HnswVectorsFormat(knnVectorsFormatParams.getMaxConnections(), knnVectorsFormatParams.getBeamWidth());
+                }
+            },
             knnScalarQuantizedVectorsFormatParams -> new Lucene99HnswScalarQuantizedVectorsFormat(
                 knnScalarQuantizedVectorsFormatParams.getMaxConnections(),
                 knnScalarQuantizedVectorsFormatParams.getBeamWidth(),

src/main/java/org/opensearch/knn/index/codec/params/KNNVectorsFormatParams.java

@@ -7,6 +7,7 @@
 
 import lombok.Getter;
 import org.opensearch.knn.common.KNNConstants;
+import org.opensearch.knn.index.SpaceType;
 
 import java.util.Map;
 
@@ -17,10 +18,16 @@
 public class KNNVectorsFormatParams {
     private int maxConnections;
     private int beamWidth;
+    private final SpaceType spaceType;
 
     public KNNVectorsFormatParams(final Map<String, Object> params, int defaultMaxConnections, int defaultBeamWidth) {
+        this(params, defaultMaxConnections, defaultBeamWidth, SpaceType.UNDEFINED);
+    }
+
+    public KNNVectorsFormatParams(final Map<String, Object> params, int defaultMaxConnections, int defaultBeamWidth, SpaceType spaceType) {
         initMaxConnections(params, defaultMaxConnections);
         initBeamWidth(params, defaultBeamWidth);
+        this.spaceType = spaceType;
     }
 
     public boolean validate(final Map<String, Object> params) {

src/main/java/org/opensearch/knn/index/engine/lucene/LuceneHNSWMethod.java

@@ -30,13 +30,18 @@
  */
 public class LuceneHNSWMethod extends AbstractKNNMethod {
 
-    private static final Set<VectorDataType> SUPPORTED_DATA_TYPES = ImmutableSet.of(VectorDataType.FLOAT, VectorDataType.BYTE);
+    private static final Set<VectorDataType> SUPPORTED_DATA_TYPES = ImmutableSet.of(
+        VectorDataType.FLOAT,
+        VectorDataType.BYTE,
+        VectorDataType.BINARY
+    );
 
     public final static List<SpaceType> SUPPORTED_SPACES = Arrays.asList(
         SpaceType.UNDEFINED,
         SpaceType.L2,
         SpaceType.COSINESIMIL,
-        SpaceType.INNER_PRODUCT
+        SpaceType.INNER_PRODUCT,
+        SpaceType.HAMMING
     );
 
     final static Encoder SQ_ENCODER = new LuceneSQEncoder();

src/main/java/org/opensearch/knn/index/query/KNNQueryFactory.java

@@ -106,6 +106,7 @@ public static Query create(CreateQueryRequest createQueryRequest) {
         log.debug(String.format("Creating Lucene k-NN query for index: %s \"\", field: %s \"\", k: %d", indexName, fieldName, k));
         switch (vectorDataType) {
             case BYTE:
+            case BINARY:
                 return getKnnByteVectorQuery(fieldName, byteVector, luceneK, filterQuery, parentFilter);
             case FLOAT:
                 return getKnnFloatVectorQuery(fieldName, vector, luceneK, filterQuery, parentFilter);

src/main/resources/META-INF/services/org.apache.lucene.codecs.KnnVectorsFormat

@@ -10,3 +10,4 @@
 #
 
 org.opensearch.knn.index.codec.KNN990Codec.NativeEngines990KnnVectorsFormat
+org.opensearch.knn.index.codec.KNN990Codec.KNN990HnswBinaryVectorsFormat