MB-64636 - Modified Weight for KNN Scorer (#2127)

1. Changed weight of a kNN query to 1 to allow the boost value to kick
in when computing query score.
Since the weight of only the kNN scorer is changed, this will not impact
how boosting is calculated for other types of queries.

To reduce the kNN score relative to the FTS query score, set boost to
<1.

2. Added a unit test which demonstrates boost increasing scores even for
pure kNN queries(kNN + match none query).

search/scorer/scorer_knn.go

@@ -128,7 +128,7 @@ func (sqs *KNNQueryScorer) Score(ctx *search.SearchContext,
 }
 
 func (sqs *KNNQueryScorer) Weight() float64 {
-	return sqs.queryBoost * sqs.queryBoost
+	return 1.0
 }
 
 func (sqs *KNNQueryScorer) SetQueryNorm(qnorm float64) {

search_knn_test.go

@@ -1208,6 +1208,97 @@ func TestSimilaritySearchMultipleSegments(t *testing.T) {
 	}
 }
 
+// Test to determine the impact of boost on kNN queries.
+func TestKNNScoreBoosting(t *testing.T) {
+	tmpIndexPath := createTmpIndexPath(t)
+	defer cleanupTmpIndexPath(t, tmpIndexPath)
+
+	const dims = 5
+	getRandomVector := func() []float32 {
+		vec := make([]float32, dims)
+		for i := 0; i < dims; i++ {
+			vec[i] = rand.Float32()
+		}
+		return vec
+	}
+
+	dataset := make([]map[string]interface{}, 10)
+
+	// Indexing just a few docs to populate index.
+	for i := 0; i < 100; i++ {
+		dataset = append(dataset, map[string]interface{}{
+			"type":    "vectorStuff",
+			"content": strconv.Itoa(i),
+			"vector":  getRandomVector(),
+		})
+	}
+
+	indexMapping := NewIndexMapping()
+	indexMapping.TypeField = "type"
+	indexMapping.DefaultAnalyzer = "en"
+	documentMapping := NewDocumentMapping()
+	indexMapping.AddDocumentMapping("vectorStuff", documentMapping)
+
+	contentFieldMapping := NewTextFieldMapping()
+	contentFieldMapping.Index = true
+	contentFieldMapping.Store = true
+	documentMapping.AddFieldMappingsAt("content", contentFieldMapping)
+
+	vecFieldMapping := mapping.NewVectorFieldMapping()
+	vecFieldMapping.Index = true
+	vecFieldMapping.Dims = 5
+	vecFieldMapping.Similarity = "dot_product"
+	documentMapping.AddFieldMappingsAt("vector", vecFieldMapping)
+
+	index, err := New(tmpIndexPath, indexMapping)
+	if err != nil {
+		t.Fatal(err)
+	}
+	defer func() {
+		err := index.Close()
+		if err != nil {
+			t.Fatal(err)
+		}
+	}()
+
+	batch := index.NewBatch()
+	for i := 0; i < len(dataset); i++ {
+		batch.Index(strconv.Itoa(i), dataset[i])
+	}
+
+	err = index.Batch(batch)
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	queryVec := getRandomVector()
+	searchRequest := NewSearchRequest(NewMatchNoneQuery())
+	searchRequest.AddKNN("vector", queryVec, 3, 1.0)
+	searchRequest.Fields = []string{"content", "vector"}
+
+	hits, _ := index.Search(searchRequest)
+	hitsMap := make(map[string]float64, 0)
+	for _, hit := range hits.Hits {
+		hitsMap[hit.ID] = (hit.Score)
+	}
+
+	searchRequest2 := NewSearchRequest(NewMatchNoneQuery())
+	searchRequest.AddKNN("vector", queryVec, 3, 10.0)
+	searchRequest.Fields = []string{"content", "vector"}
+
+	hits2, _ := index.Search(searchRequest2)
+	hitsMap2 := make(map[string]float64, 0)
+	for _, hit := range hits2.Hits {
+		hitsMap2[hit.ID] = (hit.Score)
+	}
+
+	for _, hit := range hits2.Hits {
+		if hitsMap[hit.ID] != hitsMap2[hit.ID]/10 {
+			t.Errorf("boosting not working: %v %v \n", hitsMap[hit.ID], hitsMap2[hit.ID])
+		}
+	}
+}
+
 // Test to see if KNN Operators get added right to the query.
 func TestKNNOperator(t *testing.T) {
 	tmpIndexPath := createTmpIndexPath(t)