[GitHub] [lucene] msokolov commented on a change in pull request #235: LUCENE-9614: add KnnVectorQuery implementation

[GitBox <gi...@apache.org>]

msokolov commented on a change in pull request #235:
URL: https://github.com/apache/lucene/pull/235#discussion_r686235524



##########
File path: lucene/core/src/java/org/apache/lucene/search/KnnVectorQuery.java
##########
@@ -0,0 +1,318 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.lucene.search;
+
+import static org.apache.lucene.search.DocIdSetIterator.NO_MORE_DOCS;
+
+import java.io.IOException;
+import java.util.Arrays;
+import java.util.Comparator;
+import java.util.Objects;
+import org.apache.lucene.codecs.KnnVectorsReader;
+import org.apache.lucene.document.KnnVectorField;
+import org.apache.lucene.index.IndexReader;
+import org.apache.lucene.index.LeafReaderContext;
+
+/** Uses {@link KnnVectorsReader#search} to perform nearest Neighbour search. */
+public class KnnVectorQuery extends Query {
+
+  private static final TopDocs NO_RESULTS =
+      new TopDocs(new TotalHits(0, TotalHits.Relation.EQUAL_TO), new ScoreDoc[0]);
+
+  private final String field;
+  private final float[] target;
+  private final int k;
+
+  /**
+   * Find the <code>k</code> nearest documents to the target vector according to the vectors in the
+   * given field. <code>target</code> vector.
+   *
+   * @param field a field that has been indexed as a {@link KnnVectorField}.
+   * @param target the target of the search
+   * @param k the number of documents to find
+   * @throws IllegalArgumentException if <code>k</code> is less than 1
+   */
+  public KnnVectorQuery(String field, float[] target, int k) {
+    this.field = field;
+    this.target = target;
+    this.k = k;
+    if (k < 1) {
+      throw new IllegalArgumentException("k must be at least 1, got: " + k);
+    }
+  }
+
+  @Override
+  public Query rewrite(IndexReader reader) throws IOException {
+    int boundedK = Math.min(k, reader.numDocs());
+    TopDocs[] perLeafResults = new TopDocs[reader.leaves().size()];
+    for (LeafReaderContext ctx : reader.leaves()) {
+      // Calculate kPerLeaf as an overestimate of the expected number of the closest k documents in
+      // this leaf
+      int expectedKPerLeaf = Math.max(1, boundedK * ctx.reader().numDocs() / reader.numDocs());
+      // Increase to include 3 std. deviations of a Binomial distribution.
+      int kPerLeaf = (int) (expectedKPerLeaf + 3 * Math.sqrt(expectedKPerLeaf));
+      perLeafResults[ctx.ord] = searchLeaf(ctx, kPerLeaf);
+    }
+    // Merge sort the results
+    TopDocs topK = TopDocs.merge(boundedK, perLeafResults);
+    // re-query any outlier segments (normally there should be none).
+    topK = checkForOutlierSegments(reader, topK, perLeafResults);
+    if (topK.scoreDocs.length == 0) {
+      return new MatchNoDocsQuery();
+    }
+    return createRewrittenQuery(reader, topK);
+  }
+
+  private TopDocs searchLeaf(LeafReaderContext ctx, int kPerLeaf) throws IOException {
+    TopDocs results = ctx.reader().searchNearestVectors(field, target, kPerLeaf);
+    if (results == null) {
+      return NO_RESULTS;
+    }
+    if (ctx.docBase > 0) {
+      for (ScoreDoc scoreDoc : results.scoreDocs) {
+        scoreDoc.doc += ctx.docBase;
+      }
+    }
+    return results;
+  }
+
+  private TopDocs checkForOutlierSegments(IndexReader reader, TopDocs topK, TopDocs[] perLeaf)
+      throws IOException {
+    int k = topK.scoreDocs.length;
+    if (k == 0) {
+      return topK;
+    }
+    float minScore = topK.scoreDocs[topK.scoreDocs.length - 1].score;
+    boolean rescored = false;
+    for (int i = 0; i < perLeaf.length; i++) {
+      if (perLeaf[i].scoreDocs[perLeaf[i].scoreDocs.length - 1].score >= minScore) {
+        // This segment's worst score was competitive; search it again, gathering full K this time
+        perLeaf[i] = searchLeaf(reader.leaves().get(i), topK.scoreDocs.length);
+        rescored = true;
+      }
+    }
+    if (rescored) {
+      return TopDocs.merge(k, perLeaf);
+    } else {
+      return topK;
+    }
+  }
+
+  private Query createRewrittenQuery(IndexReader reader, TopDocs topK) {
+    int len = topK.scoreDocs.length;
+    float minScore = topK.scoreDocs[len - 1].score;
+    Arrays.sort(topK.scoreDocs, Comparator.comparingInt(a -> a.doc));
+    int[] docs = new int[len];
+    float[] scores = new float[len];
+    for (int i = 0; i < len; i++) {
+      docs[i] = topK.scoreDocs[i].doc;
+      scores[i] = topK.scoreDocs[i].score - minScore; // flip negative scores
+    }
+    int[] segmentStarts = findSegmentStarts(reader, docs);
+    return new DocAndScoreQuery(docs, scores, segmentStarts);
+  }
+
+  private int[] findSegmentStarts(IndexReader reader, int[] docs) {
+    int[] starts = new int[reader.leaves().size() + 1];
+    starts[starts.length - 1] = docs.length;
+    if (starts.length == 2) {
+      return starts;
+    }
+    int resultIndex = 0;
+    for (int i = 1; i < starts.length - 1; i++) {
+      int upper = reader.leaves().get(i).docBase;
+      resultIndex = Arrays.binarySearch(docs, resultIndex, docs.length, upper);
+      if (resultIndex < 0) {
+        resultIndex = -1 - resultIndex;
+      }
+      starts[i] = resultIndex;
+    }
+    return starts;
+  }
+
+  @Override
+  public String toString(String field) {
+    return "<vector:" + this.field + "[" + target[0] + ",...][" + k + "]>";
+  }
+
+  @Override
+  public void visit(QueryVisitor visitor) {
+    if (visitor.acceptField(field)) {
+      visitor.visitLeaf(this);
+    }
+  }
+
+  @Override
+  public boolean equals(Object obj) {
+    return obj instanceof KnnVectorQuery
+        && ((KnnVectorQuery) obj).k == k
+        && ((KnnVectorQuery) obj).field.equals(field)
+        && Arrays.equals(((KnnVectorQuery) obj).target, target);
+  }
+
+  @Override
+  public int hashCode() {
+    return Objects.hash(field, k, Arrays.hashCode(target));
+  }
+
+  /** Caches the results of a KnnVector search: a list of docs and their scores */
+  class DocAndScoreQuery extends Query {
+
+    private final int[] docs;
+    private final float[] scores;
+    private final int[] segmentStarts;
+
+    /**
+     * Constructor
+     *
+     * @param docs the global docids of documents that match, in ascending order
+     * @param scores the scores of the matching documents
+     * @param segmentStarts the indexes in docs and scores corresponding to the first matching
+     *     document in each segment. If a segment has no matching documents, it should be assigned
+     *     the index of the next segment that does. There should be a final entry that is always
+     *     docs.length-1.
+     */
+    DocAndScoreQuery(int[] docs, float[] scores, int[] segmentStarts) {
+      this.docs = docs;
+      this.scores = scores;
+      this.segmentStarts = segmentStarts;
+    }
+
+    @Override
+    public Weight createWeight(IndexSearcher searcher, ScoreMode scoreMode, float boost)
+        throws IOException {
+      return new Weight(this) {
+        @Override
+        public Explanation explain(LeafReaderContext context, int doc) {
+          int found = Arrays.binarySearch(docs, doc);
+          if (found < 0) {
+            return Explanation.noMatch("not in top " + k);
+          }
+          return Explanation.match(scores[found], "within top " + k);
+        }
+
+        @Override
+        public Scorer scorer(LeafReaderContext context) {
+
+          return new Scorer(this) {
+            final int lower = segmentStarts[context.ord];
+            final int upper = segmentStarts[context.ord + 1];
+            int upTo = -1;
+
+            @Override
+            public DocIdSetIterator iterator() {
+              return new DocIdSetIterator() {
+                @Override
+                public int docID() {
+                  return docIdNoShadow();
+                }
+
+                @Override
+                public int nextDoc() {
+                  if (upTo == -1) {
+                    upTo = lower;
+                  } else {
+                    ++upTo;
+                  }
+                  return docIdNoShadow();
+                }
+
+                @Override
+                public int advance(int target) throws IOException {
+                  return slowAdvance(target);
+                }
+
+                @Override
+                public long cost() {
+                  return upper - lower;
+                }
+              };
+            }
+
+            @Override
+            public float getMaxScore(int docid) {
+              docid += context.docBase;
+              float maxScore = 0;
+              for (int idx = Math.max(0, upTo); idx < upper && docs[idx] <= docid; idx++) {
+                maxScore = Math.max(maxScore, scores[idx]);
+              }
+              return maxScore;
+            }
+
+            @Override
+            public float score() {
+              if (upTo >= lower && upTo < upper) {
+                return scores[upTo];
+              }
+              return 0;

Review comment:
       OK; I guess I wasn't totally clear on our ability to make such an assertion. I'll remove the checks and update the test I added for this edge case which I guess doesn't reflect normal usage.




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