search_knn_test.go

//  Copyright (c) 2023 Couchbase, Inc.
//
// Licensed 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.

//go:build vectors
// +build vectors

package bleve

import (
	"archive/zip"
	"bytes"
	"encoding/base64"
	"encoding/binary"
	"encoding/json"
	"fmt"
	"math"
	"math/rand"
	"reflect"
	"sort"
	"strconv"
	"sync"
	"testing"
	"time"

	"github.com/blevesearch/bleve/v2/analysis/analyzer/keyword"
	"github.com/blevesearch/bleve/v2/analysis/lang/en"
	"github.com/blevesearch/bleve/v2/index/scorch"
	"github.com/blevesearch/bleve/v2/mapping"
	"github.com/blevesearch/bleve/v2/search"
	"github.com/blevesearch/bleve/v2/search/query"
	index "github.com/blevesearch/bleve_index_api"
)

const testInputCompressedFile = "test/knn/knn_dataset_queries.zip"
const testDatasetFileName = "knn_dataset.json"
const testQueryFileName = "knn_queries.json"

const testDatasetDims = 384

var knnOperators []knnOperator = []knnOperator{knnOperatorAnd, knnOperatorOr}

func TestSimilaritySearchPartitionedIndex(t *testing.T) {
	dataset, searchRequests, err := readDatasetAndQueries(testInputCompressedFile)
	if err != nil {
		t.Fatal(err)
	}
	documents := makeDatasetIntoDocuments(dataset)
	contentFieldMapping := NewTextFieldMapping()
	contentFieldMapping.Analyzer = en.AnalyzerName

	vecFieldMappingL2 := mapping.NewVectorFieldMapping()
	vecFieldMappingL2.Dims = testDatasetDims
	vecFieldMappingL2.Similarity = index.EuclideanDistance

	indexMappingL2Norm := NewIndexMapping()
	indexMappingL2Norm.DefaultMapping.AddFieldMappingsAt("content", contentFieldMapping)
	indexMappingL2Norm.DefaultMapping.AddFieldMappingsAt("vector", vecFieldMappingL2)

	vecFieldMappingDot := mapping.NewVectorFieldMapping()
	vecFieldMappingDot.Dims = testDatasetDims
	vecFieldMappingDot.Similarity = index.InnerProduct

	indexMappingDotProduct := NewIndexMapping()
	indexMappingDotProduct.DefaultMapping.AddFieldMappingsAt("content", contentFieldMapping)
	indexMappingDotProduct.DefaultMapping.AddFieldMappingsAt("vector", vecFieldMappingDot)

	vecFieldMappingCosine := mapping.NewVectorFieldMapping()
	vecFieldMappingCosine.Dims = testDatasetDims
	vecFieldMappingCosine.Similarity = index.CosineSimilarity

	indexMappingCosine := NewIndexMapping()
	indexMappingCosine.DefaultMapping.AddFieldMappingsAt("content", contentFieldMapping)
	indexMappingCosine.DefaultMapping.AddFieldMappingsAt("vector", vecFieldMappingCosine)

	type testCase struct {
		testType           string
		queryIndex         int
		numIndexPartitions int
		mapping            mapping.IndexMapping
	}

	testCases := []testCase{
		// l2 norm similarity
		{
			testType:           "multi_partition:match_none:oneKNNreq:k=3",
			queryIndex:         0,
			numIndexPartitions: 4,
			mapping:            indexMappingL2Norm,
		},
		{
			testType:           "multi_partition:match_none:oneKNNreq:k=2",
			queryIndex:         0,
			numIndexPartitions: 10,
			mapping:            indexMappingL2Norm,
		},
		{
			testType:           "multi_partition:match:oneKNNreq:k=2",
			queryIndex:         1,
			numIndexPartitions: 5,
			mapping:            indexMappingL2Norm,
		},
		{
			testType:           "multi_partition:disjunction:twoKNNreq:k=2,2",
			queryIndex:         2,
			numIndexPartitions: 4,
			mapping:            indexMappingL2Norm,
		},
		// dot product similarity
		{
			testType:           "multi_partition:match_none:oneKNNreq:k=3",
			queryIndex:         0,
			numIndexPartitions: 4,
			mapping:            indexMappingDotProduct,
		},
		{
			testType:           "multi_partition:match_none:oneKNNreq:k=2",
			queryIndex:         0,
			numIndexPartitions: 10,
			mapping:            indexMappingDotProduct,
		},
		{
			testType:           "multi_partition:match:oneKNNreq:k=2",
			queryIndex:         1,
			numIndexPartitions: 5,
			mapping:            indexMappingDotProduct,
		},
		{
			testType:           "multi_partition:disjunction:twoKNNreq:k=2,2",
			queryIndex:         2,
			numIndexPartitions: 4,
			mapping:            indexMappingDotProduct,
		},
		// cosine similarity
		{
			testType:           "multi_partition:match_none:oneKNNreq:k=3",
			queryIndex:         0,
			numIndexPartitions: 7,
			mapping:            indexMappingCosine,
		},
		{
			testType:           "multi_partition:match_none:oneKNNreq:k=2",
			queryIndex:         0,
			numIndexPartitions: 5,
			mapping:            indexMappingCosine,
		},
		{
			testType:           "multi_partition:match:oneKNNreq:k=2",
			queryIndex:         1,
			numIndexPartitions: 3,
			mapping:            indexMappingCosine,
		},
		{
			testType:           "multi_partition:disjunction:twoKNNreq:k=2,2",
			queryIndex:         2,
			numIndexPartitions: 9,
			mapping:            indexMappingCosine,
		},
	}

	index := NewIndexAlias()
	var reqSort = search.SortOrder{&search.SortScore{Desc: true}, &search.SortDocID{Desc: true}, &search.SortField{Desc: false, Field: "content"}}
	for testCaseNum, testCase := range testCases {
		originalRequest := searchRequests[testCase.queryIndex]
		for _, operator := range knnOperators {

			index.indexes = make([]Index, 0)
			query := copySearchRequest(originalRequest, nil)
			query.AddKNNOperator(operator)
			query.Sort = reqSort.Copy()
			query.Explain = true

			nameToIndex := createPartitionedIndex(documents, index, 1, testCase.mapping, t, false)
			controlResult, err := index.Search(query)
			if err != nil {
				cleanUp(t, nameToIndex)
				t.Fatal(err)
			}
			if !finalHitsHaveValidIndex(controlResult.Hits, nameToIndex) {
				cleanUp(t, nameToIndex)
				t.Fatalf("test case #%d failed: expected control result hits to have valid `Index`", testCaseNum)
			}
			cleanUp(t, nameToIndex)

			index.indexes = make([]Index, 0)
			query = copySearchRequest(originalRequest, nil)
			query.AddKNNOperator(operator)
			query.Sort = reqSort.Copy()
			query.Explain = true

			nameToIndex = createPartitionedIndex(documents, index, testCase.numIndexPartitions, testCase.mapping, t, false)
			experimentalResult, err := index.Search(query)
			if err != nil {
				cleanUp(t, nameToIndex)
				t.Fatal(err)
			}
			if !finalHitsHaveValidIndex(experimentalResult.Hits, nameToIndex) {
				cleanUp(t, nameToIndex)
				t.Fatalf("test case #%d failed: expected experimental Result hits to have valid `Index`", testCaseNum)
			}
			verifyResult(t, controlResult, experimentalResult, testCaseNum, true)
			cleanUp(t, nameToIndex)

			index.indexes = make([]Index, 0)
			query = copySearchRequest(originalRequest, nil)
			query.AddKNNOperator(operator)
			query.Sort = reqSort.Copy()
			query.Explain = true

			nameToIndex = createPartitionedIndex(documents, index, testCase.numIndexPartitions, testCase.mapping, t, true)
			multiLevelIndexResult, err := index.Search(query)
			if err != nil {
				cleanUp(t, nameToIndex)
				t.Fatal(err)
			}
			if !finalHitsHaveValidIndex(multiLevelIndexResult.Hits, nameToIndex) {
				cleanUp(t, nameToIndex)
				t.Fatalf("test case #%d failed: expected experimental Result hits to have valid `Index`", testCaseNum)
			}
			verifyResult(t, multiLevelIndexResult, experimentalResult, testCaseNum, false)
			cleanUp(t, nameToIndex)

		}
	}

	var facets = map[string]*FacetRequest{
		"content": {
			Field: "content",
			Size:  10,
		},
	}

	index = NewIndexAlias()
	for testCaseNum, testCase := range testCases {
		index.indexes = make([]Index, 0)
		nameToIndex := createPartitionedIndex(documents, index, testCase.numIndexPartitions, testCase.mapping, t, false)
		originalRequest := searchRequests[testCase.queryIndex]
		for _, operator := range knnOperators {
			from, size := originalRequest.From, originalRequest.Size
			query := copySearchRequest(originalRequest, nil)
			query.AddKNNOperator(operator)
			query.Explain = true
			query.From = from
			query.Size = size

			// Three types of queries to run wrt sort and facet fields that require fields.
			// 1. Sort And Facet are there
			// 2. Sort is there, Facet is not there
			// 3. Sort is not there, Facet is there
			// The case where both sort and facet are not there is already covered in the previous tests.

			// 1. Sort And Facet are there
			query.Facets = facets
			query.Sort = reqSort.Copy()

			res1, err := index.Search(query)
			if err != nil {
				cleanUp(t, nameToIndex)
				t.Fatal(err)
			}
			if !finalHitsHaveValidIndex(res1.Hits, nameToIndex) {
				cleanUp(t, nameToIndex)
				t.Fatalf("test case #%d failed: expected experimental Result hits to have valid `Index`", testCaseNum)
			}

			facetRes1 := res1.Facets
			facetRes1Str, err := json.Marshal(facetRes1)
			if err != nil {
				cleanUp(t, nameToIndex)
				t.Fatal(err)
			}

			// 2. Sort is there, Facet is not there
			query.Facets = nil
			query.Sort = reqSort.Copy()

			res2, err := index.Search(query)
			if err != nil {
				cleanUp(t, nameToIndex)
				t.Fatal(err)
			}
			if !finalHitsHaveValidIndex(res2.Hits, nameToIndex) {
				cleanUp(t, nameToIndex)
				t.Fatalf("test case #%d failed: expected experimental Result hits to have valid `Index`", testCaseNum)
			}

			// 3. Sort is not there, Facet is there
			query.Facets = facets
			query.Sort = nil
			res3, err := index.Search(query)
			if err != nil {
				cleanUp(t, nameToIndex)
				t.Fatal(err)
			}
			if !finalHitsHaveValidIndex(res3.Hits, nameToIndex) {
				cleanUp(t, nameToIndex)
				t.Fatalf("test case #%d failed: expected experimental Result hits to have valid `Index`", testCaseNum)
			}

			facetRes3 := res3.Facets
			facetRes3Str, err := json.Marshal(facetRes3)
			if err != nil {
				cleanUp(t, nameToIndex)
				t.Fatal(err)
			}

			// Verify the facet results
			if string(facetRes1Str) != string(facetRes3Str) {
				cleanUp(t, nameToIndex)
				t.Fatalf("test case #%d failed: expected facet results to be equal", testCaseNum)
			}

			// Verify the results
			verifyResult(t, res1, res2, testCaseNum, false)
			verifyResult(t, res2, res3, testCaseNum, true)

			// Test early exit fail case -> matchNone + facetRequest
			query.Query = NewMatchNoneQuery()
			query.Sort = reqSort.Copy()
			// control case
			query.Facets = nil
			res4Ctrl, err := index.Search(query)
			if err != nil {
				cleanUp(t, nameToIndex)
				t.Fatal(err)
			}
			if !finalHitsHaveValidIndex(res4Ctrl.Hits, nameToIndex) {
				cleanUp(t, nameToIndex)
				t.Fatalf("test case #%d failed: expected control Result hits to have valid `Index`", testCaseNum)
			}

			// experimental case
			query.Facets = facets
			res4Exp, err := index.Search(query)
			if err != nil {
				cleanUp(t, nameToIndex)
				t.Fatal(err)
			}
			if !finalHitsHaveValidIndex(res4Exp.Hits, nameToIndex) {
				cleanUp(t, nameToIndex)
				t.Fatalf("test case #%d failed: expected experimental Result hits to have valid `Index`", testCaseNum)
			}

			if !(operator == knnOperatorAnd && res4Ctrl.Total == 0 && res4Exp.Total == 0) {
				// catch case where no hits are returned
				// due to matchNone query with a KNN request with operator AND
				// where no hits are part of the intersection in multi knn request
				verifyResult(t, res4Ctrl, res4Exp, testCaseNum, false)
			}
		}
		cleanUp(t, nameToIndex)
	}

	// Test Pagination with multi partitioned index
	index = NewIndexAlias()
	index.indexes = make([]Index, 0)
	nameToIndex := createPartitionedIndex(documents, index, 8, indexMappingL2Norm, t, true)

	// Test From + Size pagination for Hybrid Search (2-Phase)
	query := copySearchRequest(searchRequests[4], nil)
	query.Sort = reqSort.Copy()
	query.Facets = facets
	query.Explain = true

	testFromSizePagination(t, query, index, nameToIndex)

	// Test From + Size pagination for Early Exit Hybrid Search (1-Phase)
	query = copySearchRequest(searchRequests[4], nil)
	query.Query = NewMatchNoneQuery()
	query.Sort = reqSort.Copy()
	query.Facets = nil
	query.Explain = true

	testFromSizePagination(t, query, index, nameToIndex)

	cleanUp(t, nameToIndex)
}

func testFromSizePagination(t *testing.T, query *SearchRequest, index Index, nameToIndex map[string]Index) {
	query.From = 0
	query.Size = 30

	resCtrl, err := index.Search(query)
	if err != nil {
		cleanUp(t, nameToIndex)
		t.Fatal(err)
	}

	ctrlHitIds := make([]string, len(resCtrl.Hits))
	for i, doc := range resCtrl.Hits {
		ctrlHitIds[i] = doc.ID
	}
	// experimental case

	fromValues := []int{0, 5, 10, 15, 20, 25}
	size := 5
	for fromIdx := 0; fromIdx < len(fromValues); fromIdx++ {
		from := fromValues[fromIdx]
		query.From = from
		query.Size = size
		resExp, err := index.Search(query)
		if err != nil {
			cleanUp(t, nameToIndex)
			t.Fatal(err)
		}
		if from >= len(ctrlHitIds) {
			if len(resExp.Hits) != 0 {
				cleanUp(t, nameToIndex)
				t.Fatalf("expected 0 hits, got %d", len(resExp.Hits))
			}
			continue
		}
		numHitsExp := len(resExp.Hits)
		numHitsCtrl := min(len(ctrlHitIds)-from, size)
		if numHitsExp != numHitsCtrl {
			cleanUp(t, nameToIndex)
			t.Fatalf("expected %d hits, got %d", numHitsCtrl, numHitsExp)
		}
		for i := 0; i < numHitsExp; i++ {
			doc := resExp.Hits[i]
			startOffset := from + i
			if doc.ID != ctrlHitIds[startOffset] {
				cleanUp(t, nameToIndex)
				t.Fatalf("expected %s at index %d, got %s", ctrlHitIds[startOffset], i, doc.ID)
			}
		}
	}
}

func TestVectorBase64Index(t *testing.T) {
	dataset, searchRequests, err := readDatasetAndQueries(testInputCompressedFile)
	if err != nil {
		t.Fatal(err)
	}
	documents := makeDatasetIntoDocuments(dataset)

	_, searchRequestsCopy, err := readDatasetAndQueries(testInputCompressedFile)
	if err != nil {
		t.Fatal(err)
	}

	for _, doc := range documents {
		vec, ok := doc["vector"].([]float32)
		if !ok {
			t.Fatal("Typecasting vector to float array failed")
		}

		buf := new(bytes.Buffer)
		for _, v := range vec {
			err := binary.Write(buf, binary.LittleEndian, v)
			if err != nil {
				t.Fatal(err)
			}
		}

		doc["vectorEncoded"] = base64.StdEncoding.EncodeToString(buf.Bytes())
	}

	for _, sr := range searchRequestsCopy {
		for _, kr := range sr.KNN {
			kr.Field = "vectorEncoded"
		}
	}

	contentFM := NewTextFieldMapping()
	contentFM.Analyzer = en.AnalyzerName

	vecFML2 := mapping.NewVectorFieldMapping()
	vecFML2.Dims = testDatasetDims
	vecFML2.Similarity = index.EuclideanDistance

	vecBFML2 := mapping.NewVectorBase64FieldMapping()
	vecBFML2.Dims = testDatasetDims
	vecBFML2.Similarity = index.EuclideanDistance

	vecFMDot := mapping.NewVectorFieldMapping()
	vecFMDot.Dims = testDatasetDims
	vecFMDot.Similarity = index.InnerProduct

	vecBFMDot := mapping.NewVectorBase64FieldMapping()
	vecBFMDot.Dims = testDatasetDims
	vecBFMDot.Similarity = index.InnerProduct

	indexMappingL2 := NewIndexMapping()
	indexMappingL2.DefaultMapping.AddFieldMappingsAt("content", contentFM)
	indexMappingL2.DefaultMapping.AddFieldMappingsAt("vector", vecFML2)
	indexMappingL2.DefaultMapping.AddFieldMappingsAt("vectorEncoded", vecBFML2)

	indexMappingDot := NewIndexMapping()
	indexMappingDot.DefaultMapping.AddFieldMappingsAt("content", contentFM)
	indexMappingDot.DefaultMapping.AddFieldMappingsAt("vector", vecFMDot)
	indexMappingDot.DefaultMapping.AddFieldMappingsAt("vectorEncoded", vecBFMDot)

	tmpIndexPathL2 := createTmpIndexPath(t)
	defer cleanupTmpIndexPath(t, tmpIndexPathL2)

	tmpIndexPathDot := createTmpIndexPath(t)
	defer cleanupTmpIndexPath(t, tmpIndexPathDot)

	indexL2, err := New(tmpIndexPathL2, indexMappingL2)
	if err != nil {
		t.Fatal(err)
	}
	defer func() {
		err := indexL2.Close()
		if err != nil {
			t.Fatal(err)
		}
	}()

	indexDot, err := New(tmpIndexPathDot, indexMappingDot)
	if err != nil {
		t.Fatal(err)
	}
	defer func() {
		err := indexDot.Close()
		if err != nil {
			t.Fatal(err)
		}
	}()

	batchL2 := indexL2.NewBatch()
	batchDot := indexDot.NewBatch()

	for _, doc := range documents {
		err = batchL2.Index(doc["id"].(string), doc)
		if err != nil {
			t.Fatal(err)
		}
		err = batchDot.Index(doc["id"].(string), doc)
		if err != nil {
			t.Fatal(err)
		}
	}

	err = indexL2.Batch(batchL2)
	if err != nil {
		t.Fatal(err)
	}

	err = indexDot.Batch(batchDot)
	if err != nil {
		t.Fatal(err)
	}

	for i := range searchRequests {
		for _, operator := range knnOperators {
			controlQuery := searchRequests[i]
			testQuery := searchRequestsCopy[i]

			controlQuery.AddKNNOperator(operator)
			testQuery.AddKNNOperator(operator)

			controlResultL2, err := indexL2.Search(controlQuery)
			if err != nil {
				t.Fatal(err)
			}
			testResultL2, err := indexL2.Search(testQuery)
			if err != nil {
				t.Fatal(err)
			}

			if controlResultL2 != nil && testResultL2 != nil {
				if len(controlResultL2.Hits) == len(testResultL2.Hits) {
					for j := range controlResultL2.Hits {
						if controlResultL2.Hits[j].ID != testResultL2.Hits[j].ID {
							t.Fatalf("testcase %d failed: expected hit id %s, got hit id %s", i, controlResultL2.Hits[j].ID, testResultL2.Hits[j].ID)
						}
					}
				}
			} else if (controlResultL2 == nil && testResultL2 != nil) ||
				(controlResultL2 != nil && testResultL2 == nil) {
				t.Fatalf("testcase %d failed: expected result %s, got result %s", i, controlResultL2, testResultL2)
			}

			controlResultDot, err := indexDot.Search(controlQuery)
			if err != nil {
				t.Fatal(err)
			}
			testResultDot, err := indexDot.Search(testQuery)
			if err != nil {
				t.Fatal(err)
			}

			if controlResultDot != nil && testResultDot != nil {
				if len(controlResultDot.Hits) == len(testResultDot.Hits) {
					for j := range controlResultDot.Hits {
						if controlResultDot.Hits[j].ID != testResultDot.Hits[j].ID {
							t.Fatalf("testcase %d failed: expected hit id %s, got hit id %s", i, controlResultDot.Hits[j].ID, testResultDot.Hits[j].ID)
						}
					}
				}
			} else if (controlResultDot == nil && testResultDot != nil) ||
				(controlResultDot != nil && testResultDot == nil) {
				t.Fatalf("testcase %d failed: expected result %s, got result %s", i, controlResultDot, testResultDot)
			}
		}
	}
}

// Test to verify that the bivf-flat indexes with vector base64 field mapping returns the
// same results as the non-optimized vector field mapping for L2, Dot Product and Cosine similarities.
// Also test to see no differences in results for any distance metric
func TestVectorBivfIndexes(t *testing.T) {
	optimizations := []string{index.IndexBIVFWithBackingSQ8, index.IndexBIVFWithBackingFlat}
	for _, optimization := range optimizations {
		testVectorBivfIndex(t, optimization)
	}
}

func testVectorBivfIndex(t *testing.T, optimization string) {

	dataset, searchRequests, err := readDatasetAndQueries(testInputCompressedFile)
	if err != nil {
		t.Fatal(err)
	}
	documents := makeDatasetIntoDocuments(dataset)

	_, searchRequestsCopy, err := readDatasetAndQueries(testInputCompressedFile)
	if err != nil {
		t.Fatal(err)
	}

	for _, doc := range documents {
		vec, ok := doc["vector"].([]float32)
		if !ok {
			t.Fatal("Typecasting vector to float array failed")
		}

		buf := new(bytes.Buffer)
		for _, v := range vec {
			err := binary.Write(buf, binary.LittleEndian, v)
			if err != nil {
				t.Fatal(err)
			}
		}

		doc["vectorEncoded"] = base64.StdEncoding.EncodeToString(buf.Bytes())
	}

	for _, sr := range searchRequestsCopy {
		for _, kr := range sr.KNN {
			kr.Field = "vectorEncoded"
		}
	}

	contentFM := NewTextFieldMapping()
	contentFM.Analyzer = en.AnalyzerName

	vecFML2 := mapping.NewVectorFieldMapping()
	vecFML2.Dims = testDatasetDims
	vecFML2.Similarity = index.EuclideanDistance
	vecFML2.VectorIndexOptimizedFor = optimization

	vecBFML2 := mapping.NewVectorBase64FieldMapping()
	vecBFML2.Dims = testDatasetDims
	vecBFML2.Similarity = index.EuclideanDistance
	vecBFML2.VectorIndexOptimizedFor = optimization

	vecFMDot := mapping.NewVectorFieldMapping()
	vecFMDot.Dims = testDatasetDims
	vecFMDot.Similarity = index.InnerProduct
	vecFMDot.VectorIndexOptimizedFor = optimization

	vecBFMDot := mapping.NewVectorBase64FieldMapping()
	vecBFMDot.Dims = testDatasetDims
	vecBFMDot.Similarity = index.InnerProduct
	vecBFMDot.VectorIndexOptimizedFor = optimization

	vecFMCosine := mapping.NewVectorFieldMapping()
	vecFMCosine.Dims = testDatasetDims
	vecFMCosine.Similarity = index.CosineSimilarity

	vecBFMCosine := mapping.NewVectorBase64FieldMapping()
	vecBFMCosine.Dims = testDatasetDims
	vecBFMCosine.Similarity = index.CosineSimilarity
	vecBFMCosine.VectorIndexOptimizedFor = optimization

	indexMappingL2 := NewIndexMapping()
	indexMappingL2.DefaultMapping.AddFieldMappingsAt("content", contentFM)
	indexMappingL2.DefaultMapping.AddFieldMappingsAt("vector", vecFML2)
	indexMappingL2.DefaultMapping.AddFieldMappingsAt("vectorEncoded", vecBFML2)

	indexMappingDot := NewIndexMapping()
	indexMappingDot.DefaultMapping.AddFieldMappingsAt("content", contentFM)
	indexMappingDot.DefaultMapping.AddFieldMappingsAt("vector", vecFMDot)
	indexMappingDot.DefaultMapping.AddFieldMappingsAt("vectorEncoded", vecBFMDot)

	indexMappingCosine := NewIndexMapping()
	indexMappingCosine.DefaultMapping.AddFieldMappingsAt("content", contentFM)
	indexMappingCosine.DefaultMapping.AddFieldMappingsAt("vector", vecFMCosine)
	indexMappingCosine.DefaultMapping.AddFieldMappingsAt("vectorEncoded", vecBFMCosine)

	tmpIndexPathL2 := createTmpIndexPath(t)
	defer cleanupTmpIndexPath(t, tmpIndexPathL2)

	tmpIndexPathDot := createTmpIndexPath(t)
	defer cleanupTmpIndexPath(t, tmpIndexPathDot)

	tmpIndexPathCosine := createTmpIndexPath(t)
	defer cleanupTmpIndexPath(t, tmpIndexPathCosine)

	indexL2, err := New(tmpIndexPathL2, indexMappingL2)
	if err != nil {
		t.Fatal(err)
	}
	defer func() {
		err := indexL2.Close()
		if err != nil {
			t.Fatal(err)
		}
	}()

	indexDot, err := New(tmpIndexPathDot, indexMappingDot)
	if err != nil {
		t.Fatal(err)
	}
	defer func() {
		err := indexDot.Close()
		if err != nil {
			t.Fatal(err)
		}
	}()

	indexCosine, err := New(tmpIndexPathCosine, indexMappingCosine)
	if err != nil {
		t.Fatal(err)
	}
	defer func() {
		err := indexCosine.Close()
		if err != nil {
			t.Fatal(err)
		}
	}()

	batchL2 := indexL2.NewBatch()
	batchDot := indexDot.NewBatch()
	batchCosine := indexCosine.NewBatch()

	for _, doc := range documents {
		err = batchL2.Index(doc["id"].(string), doc)
		if err != nil {
			t.Fatal(err)
		}
		err = batchDot.Index(doc["id"].(string), doc)
		if err != nil {
			t.Fatal(err)
		}
		err = batchCosine.Index(doc["id"].(string), doc)
		if err != nil {
			t.Fatal(err)
		}
	}

	err = indexL2.Batch(batchL2)
	if err != nil {
		t.Fatal(err)
	}

	err = indexDot.Batch(batchDot)
	if err != nil {
		t.Fatal(err)
	}

	err = indexCosine.Batch(batchCosine)
	if err != nil {
		t.Fatal(err)
	}

	for i := range searchRequests {
		for _, operator := range knnOperators {
			normQuery := searchRequests[i]
			base64Query := searchRequestsCopy[i]

			normQuery.AddKNNOperator(operator)
			base64Query.AddKNNOperator(operator)

			normResultL2, err := indexL2.Search(normQuery)
			if err != nil {
				t.Fatal(err)
			}
			base64ResultL2, err := indexL2.Search(base64Query)
			if err != nil {
				t.Fatal(err)
			}

			if normResultL2 != nil && base64ResultL2 != nil {
				if len(normResultL2.Hits) == len(base64ResultL2.Hits) {
					for j := range normResultL2.Hits {
						if normResultL2.Hits[j].ID != base64ResultL2.Hits[j].ID {
							t.Fatalf("testcase %d failed: expected hit id %s, got hit id %s", i, normResultL2.Hits[j].ID, base64ResultL2.Hits[j].ID)
						}
					}
				}
			} else if (normResultL2 == nil && base64ResultL2 != nil) ||
				(normResultL2 != nil && base64ResultL2 == nil) {
				t.Fatalf("testcase %d failed: expected result %s, got result %s", i, normResultL2, base64ResultL2)
			}

			normResultDot, err := indexDot.Search(normQuery)
			if err != nil {
				t.Fatal(err)
			}
			base64ResultDot, err := indexDot.Search(base64Query)
			if err != nil {
				t.Fatal(err)
			}

			if normResultDot != nil && base64ResultDot != nil {
				if len(normResultDot.Hits) == len(base64ResultDot.Hits) {
					for j := range normResultDot.Hits {
						if normResultDot.Hits[j].ID != base64ResultDot.Hits[j].ID {
							t.Fatalf("testcase %d failed: expected hit id %s, got hit id %s", i, normResultDot.Hits[j].ID, base64ResultDot.Hits[j].ID)
						}
					}
				}
			} else if (normResultDot == nil && base64ResultDot != nil) ||
				(normResultDot != nil && base64ResultDot == nil) {
				t.Fatalf("testcase %d failed: expected result %s, got result %s", i, normResultDot, base64ResultDot)
			}

			normResultCosine, err := indexCosine.Search(normQuery)
			if err != nil {
				t.Fatal(err)
			}
			base64ResultCosine, err := indexCosine.Search(base64Query)
			if err != nil {
				t.Fatal(err)
			}

			if normResultCosine != nil && base64ResultCosine != nil {
				if len(normResultCosine.Hits) == len(base64ResultCosine.Hits) {
					for j := range normResultCosine.Hits {
						if normResultCosine.Hits[j].ID != base64ResultCosine.Hits[j].ID {
							t.Fatalf("testcase %d failed: expected hit id %s, got hit id %s", i, normResultCosine.Hits[j].ID, base64ResultCosine.Hits[j].ID)
						}
					}
				}
			} else if (normResultCosine == nil && base64ResultCosine != nil) ||
				(normResultCosine != nil && base64ResultCosine == nil) {
				t.Fatalf("testcase %d failed: expected result %s, got result %s", i, normResultCosine, base64ResultCosine)
			}

			if normResultCosine != nil && normResultL2 != nil {
				if len(normResultCosine.Hits) == len(normResultL2.Hits) {
					for j := range normResultCosine.Hits {
						if normResultCosine.Hits[j].ID != normResultL2.Hits[j].ID {
							if normResultCosine.Hits[j].Score != normResultL2.Hits[j].Score {
								t.Fatalf("testcase %d failed: expected hit id %s, got hit id %s", i, normResultCosine.Hits[j].ID, normResultL2.Hits[j].ID)
							}
						}
					}
				}
			} else if (normResultCosine == nil && normResultL2 != nil) ||
				(normResultCosine != nil && normResultL2 == nil) {
				t.Fatalf("testcase %d failed: expected result %s, got result %s", i, normResultCosine, normResultL2)
			}

			if normResultCosine != nil && normResultDot != nil {
				if len(normResultCosine.Hits) == len(normResultDot.Hits) {
					for j := range normResultCosine.Hits {
						if normResultCosine.Hits[j].ID != normResultDot.Hits[j].ID {
							if normResultCosine.Hits[j].Score != normResultDot.Hits[j].Score {
								t.Fatalf("testcase %d failed: expected hit id %s, got hit id %s", i, normResultCosine.Hits[j].ID, normResultDot.Hits[j].ID)
							}
						}
					}
				}
			} else if (normResultCosine == nil && normResultDot != nil) ||
				(normResultCosine != nil && normResultDot == nil) {
				t.Fatalf("testcase %d failed: expected result %s, got result %s", i, normResultCosine, normResultDot)
			}
		}
	}
}

type testDocument struct {
	ID      string    `json:"id"`
	Content string    `json:"content"`
	Vector  []float32 `json:"vector"`
}

func readDatasetAndQueries(fileName string) ([]testDocument, []*SearchRequest, error) {
	// Open the zip archive for reading
	r, err := zip.OpenReader(fileName)
	if err != nil {
		return nil, nil, err
	}
	var dataset []testDocument
	var queries []*SearchRequest

	defer r.Close()
	for _, f := range r.File {
		jsonFile, err := f.Open()
		if err != nil {
			return nil, nil, err
		}
		defer jsonFile.Close()
		if f.Name == testDatasetFileName {
			err = json.NewDecoder(jsonFile).Decode(&dataset)
			if err != nil {
				return nil, nil, err
			}
		} else if f.Name == testQueryFileName {
			err = json.NewDecoder(jsonFile).Decode(&queries)
			if err != nil {
				return nil, nil, err
			}
		}
	}
	return dataset, queries, nil
}

func makeDatasetIntoDocuments(dataset []testDocument) []map[string]interface{} {
	documents := make([]map[string]interface{}, len(dataset))
	for i := 0; i < len(dataset); i++ {
		document := make(map[string]interface{})
		document["id"] = dataset[i].ID
		document["content"] = dataset[i].Content
		document["vector"] = dataset[i].Vector
		documents[i] = document
	}
	return documents
}

func cleanUp(t *testing.T, nameToIndex map[string]Index) {
	for path, childIndex := range nameToIndex {
		err := childIndex.Close()
		if err != nil {
			t.Fatal(err)
		}
		cleanupTmpIndexPath(t, path)
	}
}

func createChildIndex(docs []map[string]interface{}, mapping mapping.IndexMapping, t *testing.T, nameToIndex map[string]Index) Index {
	tmpIndexPath := createTmpIndexPath(t)
	index, err := New(tmpIndexPath, mapping)
	if err != nil {
		t.Fatal(err)
	}
	nameToIndex[index.Name()] = index
	batch := index.NewBatch()
	for _, doc := range docs {
		err := batch.Index(doc["id"].(string), doc)
		if err != nil {
			cleanUp(t, nameToIndex)
			t.Fatal(err)
		}
	}
	err = index.Batch(batch)
	if err != nil {
		cleanUp(t, nameToIndex)
		t.Fatal(err)
	}
	return index
}

func createPartitionedIndex(documents []map[string]interface{}, index *indexAliasImpl, numPartitions int,
	mapping mapping.IndexMapping, t *testing.T, multiLevel bool) map[string]Index {

	partitionSize := len(documents) / numPartitions
	extraDocs := len(documents) % numPartitions
	numDocsPerPartition := make([]int, numPartitions)
	for i := 0; i < numPartitions; i++ {
		numDocsPerPartition[i] = partitionSize
		if extraDocs > 0 {
			numDocsPerPartition[i]++
			extraDocs--
		}
	}
	docsPerPartition := make([][]map[string]interface{}, numPartitions)
	prevCutoff := 0
	for i := 0; i < numPartitions; i++ {
		docsPerPartition[i] = make([]map[string]interface{}, numDocsPerPartition[i])
		for j := 0; j < numDocsPerPartition[i]; j++ {
			docsPerPartition[i][j] = documents[prevCutoff+j]
		}
		prevCutoff += numDocsPerPartition[i]
	}

	rv := make(map[string]Index)
	if !multiLevel {
		// all indexes are at the same level
		for i := 0; i < numPartitions; i++ {
			index.Add(createChildIndex(docsPerPartition[i], mapping, t, rv))
		}
	} else {
		// alias tree
		indexes := make([]Index, numPartitions)
		for i := 0; i < numPartitions; i++ {
			indexes[i] = createChildIndex(docsPerPartition[i], mapping, t, rv)
		}
		numAlias := int(math.Ceil(float64(numPartitions) / 2.0))
		aliases := make([]IndexAlias, numAlias)
		for i := 0; i < numAlias; i++ {
			aliases[i] = NewIndexAlias()
			aliases[i].SetName(fmt.Sprintf("alias%d", i))
			for j := 0; j < 2; j++ {
				if i*2+j < numPartitions {
					aliases[i].Add(indexes[i*2+j])
				}
			}
		}
		for i := 0; i < numAlias; i++ {
			index.Add(aliases[i])
		}
	}
	return rv
}

func createMultipleSegmentsIndex(documents []map[string]interface{}, index Index, numSegments int) error {
	// create multiple batches to simulate more than one segment
	numBatches := numSegments

	batches := make([]*Batch, numBatches)
	numDocsPerBatch := len(documents) / numBatches
	extraDocs := len(documents) % numBatches

	docsPerBatch := make([]int, numBatches)
	for i := 0; i < numBatches; i++ {
		docsPerBatch[i] = numDocsPerBatch
		if extraDocs > 0 {
			docsPerBatch[i]++
			extraDocs--
		}
	}
	prevCutoff := 0
	for i := 0; i < numBatches; i++ {
		batches[i] = index.NewBatch()
		for j := prevCutoff; j < prevCutoff+docsPerBatch[i]; j++ {
			doc := documents[j]
			err := batches[i].Index(doc["id"].(string), doc)
			if err != nil {
				return err
			}
		}
		prevCutoff += docsPerBatch[i]
	}
	errMutex := sync.Mutex{}
	var errors []error
	wg := sync.WaitGroup{}
	wg.Add(len(batches))
	for i, batch := range batches {
		go func(ix int, batchx *Batch) {
			defer wg.Done()
			err := index.Batch(batchx)
			if err != nil {
				errMutex.Lock()
				errors = append(errors, err)
				errMutex.Unlock()
			}
		}(i, batch)
	}
	wg.Wait()
	if len(errors) > 0 {
		return errors[0]
	}
	return nil
}

func truncateScore(score float64) float64 {
	epsilon := 1e-4
	truncated := float64(int(score*1e6)) / 1e6
	if math.Abs(truncated-1.0) <= epsilon {
		return 1.0
	}
	return truncated
}

// Function to compare two Explanation structs recursively
func compareExplanation(a, b *search.Explanation) bool {
	if a == nil && b == nil {
		return true
	}
	if a == nil || b == nil {
		return false
	}

	if truncateScore(a.Value) != truncateScore(b.Value) || len(a.Children) != len(b.Children) {
		return false
	}

	// Sort the children slices before comparison
	sortChildren(a.Children)
	sortChildren(b.Children)

	for i := range a.Children {
		if !compareExplanation(a.Children[i], b.Children[i]) {
			return false
		}
	}
	return true
}

// Function to sort the children slices
func sortChildren(children []*search.Explanation) {
	sort.Slice(children, func(i, j int) bool {
		return children[i].Value < children[j].Value
	})
}

// All hits from a hybrid search/knn search should not have
// index names or score breakdown.
func finalHitsOmitKNNMetadata(hits []*search.DocumentMatch) bool {
	for _, hit := range hits {
		if hit.IndexNames != nil || hit.ScoreBreakdown != nil {
			return false
		}
	}
	return true
}

func finalHitsHaveValidIndex(hits []*search.DocumentMatch, indexes map[string]Index) bool {
	for _, hit := range hits {
		if hit.Index == "" {
			return false
		}
		var idx Index
		var ok bool
		if idx, ok = indexes[hit.Index]; !ok {
			return false
		}
		if idx == nil {
			return false
		}
		var doc index.Document
		doc, err = idx.Document(hit.ID)
		if err != nil {
			return false
		}
		if doc == nil {
			return false
		}
	}
	return true
}

func verifyResult(t *testing.T, controlResult *SearchResult, experimentalResult *SearchResult, testCaseNum int, verifyOnlyDocIDs bool) {
	if controlResult.Hits.Len() == 0 || experimentalResult.Hits.Len() == 0 {
		t.Fatalf("test case #%d failed: 0 hits returned", testCaseNum)
	}
	if len(controlResult.Hits) != len(experimentalResult.Hits) {
		t.Fatalf("test case #%d failed: expected %d results, got %d", testCaseNum, len(controlResult.Hits), len(experimentalResult.Hits))
	}
	if controlResult.Total != experimentalResult.Total {
		t.Fatalf("test case #%d failed: expected total hits to be %d, got %d", testCaseNum, controlResult.Total, experimentalResult.Total)
	}
	// KNN Metadata -> Score Breakdown and IndexNames MUST be omitted from the final hits
	if !finalHitsOmitKNNMetadata(controlResult.Hits) || !finalHitsOmitKNNMetadata(experimentalResult.Hits) {
		t.Fatalf("test case #%d failed: expected no KNN metadata in hits", testCaseNum)
	}
	if controlResult.Took == 0 || experimentalResult.Took == 0 {
		t.Fatalf("test case #%d failed: expected non-zero took time", testCaseNum)
	}
	if controlResult.Request == nil || experimentalResult.Request == nil {
		t.Fatalf("test case #%d failed: expected non-nil request", testCaseNum)
	}
	if verifyOnlyDocIDs {
		// in multi partitioned index, we cannot be sure of the score or the ordering of the hits as the tf-idf scores are localized to each partition
		// so we only check the ids
		controlMap := make(map[string]struct{})
		experimentalMap := make(map[string]struct{})
		for _, hit := range controlResult.Hits {
			controlMap[hit.ID] = struct{}{}
		}
		for _, hit := range experimentalResult.Hits {
			experimentalMap[hit.ID] = struct{}{}
		}
		if len(controlMap) != len(experimentalMap) {
			t.Fatalf("test case #%d failed: expected %d results, got %d", testCaseNum, len(controlMap), len(experimentalMap))
		}
		for id := range controlMap {
			if _, ok := experimentalMap[id]; !ok {
				t.Fatalf("test case #%d failed: expected id %s to be in experimental result", testCaseNum, id)
			}
		}
		return
	}
	for i := 0; i < len(controlResult.Hits); i++ {
		if controlResult.Hits[i].ID != experimentalResult.Hits[i].ID {
			t.Fatalf("test case #%d failed: expected hit %d to have id %s, got %s", testCaseNum, i, controlResult.Hits[i].ID, experimentalResult.Hits[i].ID)
		}
		// Truncate to 6 decimal places
		actualScore := truncateScore(experimentalResult.Hits[i].Score)
		expectScore := truncateScore(controlResult.Hits[i].Score)
		if expectScore != actualScore {
			t.Fatalf("test case #%d failed: expected hit %d to have score %f, got %f", testCaseNum, i, expectScore, actualScore)
		}
		if !compareExplanation(controlResult.Hits[i].Expl, experimentalResult.Hits[i].Expl) {
			t.Fatalf("test case #%d failed: expected hit %d to have explanation %v, got %v", testCaseNum, i, controlResult.Hits[i].Expl, experimentalResult.Hits[i].Expl)
		}
	}
	if truncateScore(controlResult.MaxScore) != truncateScore(experimentalResult.MaxScore) {
		t.Fatalf("test case #%d: expected maxScore to be %f, got %f", testCaseNum, controlResult.MaxScore, experimentalResult.MaxScore)
	}
}

func TestSimilaritySearchMultipleSegments(t *testing.T) {
	// using scorch options to prevent merges during the course of this test
	// so that the knnCollector can be accurately tested
	scorch.DefaultMemoryPressurePauseThreshold = 0
	scorch.DefaultMinSegmentsForInMemoryMerge = math.MaxInt
	dataset, searchRequests, err := readDatasetAndQueries(testInputCompressedFile)
	if err != nil {
		t.Fatal(err)
	}
	documents := makeDatasetIntoDocuments(dataset)

	contentFieldMapping := NewTextFieldMapping()
	contentFieldMapping.Analyzer = en.AnalyzerName

	vecFieldMappingL2 := mapping.NewVectorFieldMapping()
	vecFieldMappingL2.Dims = testDatasetDims
	vecFieldMappingL2.Similarity = index.EuclideanDistance

	vecFieldMappingDot := mapping.NewVectorFieldMapping()
	vecFieldMappingDot.Dims = testDatasetDims
	vecFieldMappingDot.Similarity = index.InnerProduct

	vecFieldMappingCosine := mapping.NewVectorFieldMapping()
	vecFieldMappingCosine.Dims = testDatasetDims
	vecFieldMappingCosine.Similarity = index.CosineSimilarity

	indexMappingL2Norm := NewIndexMapping()
	indexMappingL2Norm.DefaultMapping.AddFieldMappingsAt("content", contentFieldMapping)
	indexMappingL2Norm.DefaultMapping.AddFieldMappingsAt("vector", vecFieldMappingL2)

	indexMappingDotProduct := NewIndexMapping()
	indexMappingDotProduct.DefaultMapping.AddFieldMappingsAt("content", contentFieldMapping)
	indexMappingDotProduct.DefaultMapping.AddFieldMappingsAt("vector", vecFieldMappingDot)

	indexMappingCosine := NewIndexMapping()
	indexMappingCosine.DefaultMapping.AddFieldMappingsAt("content", contentFieldMapping)
	indexMappingCosine.DefaultMapping.AddFieldMappingsAt("vector", vecFieldMappingCosine)

	var reqSort = search.SortOrder{&search.SortScore{Desc: true}, &search.SortDocID{Desc: true}, &search.SortField{Desc: false, Field: "content"}}

	testCases := []struct {
		numSegments int
		queryIndex  int
		mapping     mapping.IndexMapping
		scoreValue  string
	}{
		// L2 norm similarity
		{
			numSegments: 6,
			queryIndex:  0,
			mapping:     indexMappingL2Norm,
		},
		{
			numSegments: 7,
			queryIndex:  1,
			mapping:     indexMappingL2Norm,
		},
		{
			numSegments: 8,
			queryIndex:  2,
			mapping:     indexMappingL2Norm,
		},
		{
			numSegments: 9,
			queryIndex:  3,
			mapping:     indexMappingL2Norm,
		},
		{
			numSegments: 10,
			queryIndex:  4,
			mapping:     indexMappingL2Norm,
		},
		{
			numSegments: 11,
			queryIndex:  5,
			mapping:     indexMappingL2Norm,
		},
		// dot_product similarity
		{
			numSegments: 6,
			queryIndex:  0,
			mapping:     indexMappingDotProduct,
		},
		{
			numSegments: 7,
			queryIndex:  1,
			mapping:     indexMappingDotProduct,
		},
		{
			numSegments: 8,
			queryIndex:  2,
			mapping:     indexMappingDotProduct,
		},
		{
			numSegments: 9,
			queryIndex:  3,
			mapping:     indexMappingDotProduct,
		},
		{
			numSegments: 10,
			queryIndex:  4,
			mapping:     indexMappingDotProduct,
		},
		{
			numSegments: 11,
			queryIndex:  5,
			mapping:     indexMappingDotProduct,
		},
		// cosine similarity
		{
			numSegments: 9,
			queryIndex:  0,
			mapping:     indexMappingCosine,
		},
		{
			numSegments: 5,
			queryIndex:  1,
			mapping:     indexMappingCosine,
		},
		{
			numSegments: 4,
			queryIndex:  2,
			mapping:     indexMappingCosine,
		},
		{
			numSegments: 12,
			queryIndex:  3,
			mapping:     indexMappingCosine,
		},
		{
			numSegments: 7,
			queryIndex:  4,
			mapping:     indexMappingCosine,
		},
		{
			numSegments: 11,
			queryIndex:  5,
			mapping:     indexMappingCosine,
		},
		// score none test
		{
			numSegments: 3,
			queryIndex:  0,
			mapping:     indexMappingL2Norm,
			scoreValue:  "none",
		},
		{
			numSegments: 7,
			queryIndex:  1,
			mapping:     indexMappingL2Norm,
			scoreValue:  "none",
		},
		{
			numSegments: 8,
			queryIndex:  2,
			mapping:     indexMappingL2Norm,
			scoreValue:  "none",
		},
		{
			numSegments: 3,
			queryIndex:  0,
			mapping:     indexMappingDotProduct,
			scoreValue:  "none",
		},
		{
			numSegments: 7,
			queryIndex:  1,
			mapping:     indexMappingDotProduct,
			scoreValue:  "none",
		},
		{
			numSegments: 8,
			queryIndex:  2,
			mapping:     indexMappingDotProduct,
			scoreValue:  "none",
		},
		{
			numSegments: 3,
			queryIndex:  0,
			mapping:     indexMappingCosine,
			scoreValue:  "none",
		},
		{
			numSegments: 7,
			queryIndex:  1,
			mapping:     indexMappingCosine,
			scoreValue:  "none",
		},
		{
			numSegments: 8,
			queryIndex:  2,
			mapping:     indexMappingCosine,
			scoreValue:  "none",
		},
	}
	for testCaseNum, testCase := range testCases {
		originalRequest := searchRequests[testCase.queryIndex]
		for _, operator := range knnOperators {
			// run single segment test first
			tmpIndexPath := createTmpIndexPath(t)
			index, err := New(tmpIndexPath, testCase.mapping)
			if err != nil {
				t.Fatal(err)
			}
			query := copySearchRequest(originalRequest, nil)
			query.Sort = reqSort.Copy()
			query.AddKNNOperator(operator)
			query.Explain = true

			nameToIndex := make(map[string]Index)
			nameToIndex[index.Name()] = index

			err = createMultipleSegmentsIndex(documents, index, 1)
			if err != nil {
				cleanUp(t, nameToIndex)
				t.Fatal(err)
			}
			controlResult, err := index.Search(query)
			if err != nil {
				cleanUp(t, nameToIndex)
				t.Fatal(err)
			}
			if !finalHitsHaveValidIndex(controlResult.Hits, nameToIndex) {
				cleanUp(t, nameToIndex)
				t.Fatalf("test case #%d failed: expected control result hits to have valid `Index`", testCaseNum)
			}
			if testCase.scoreValue == "none" {

				query := copySearchRequest(originalRequest, nil)
				query.Sort = reqSort.Copy()
				query.AddKNNOperator(operator)
				query.Explain = true
				query.Score = testCase.scoreValue

				expectedResultScoreNone, err := index.Search(query)
				if err != nil {
					cleanUp(t, nameToIndex)
					t.Fatal(err)
				}
				if !finalHitsHaveValidIndex(expectedResultScoreNone.Hits, nameToIndex) {
					cleanUp(t, nameToIndex)
					t.Fatalf("test case #%d failed: expected score none hits to have valid `Index`", testCaseNum)
				}
				verifyResult(t, controlResult, expectedResultScoreNone, testCaseNum, true)
			}
			cleanUp(t, nameToIndex)

			// run multiple segments test
			tmpIndexPath = createTmpIndexPath(t)
			index, err = New(tmpIndexPath, testCase.mapping)
			if err != nil {
				t.Fatal(err)
			}
			nameToIndex = make(map[string]Index)
			nameToIndex[index.Name()] = index
			err = createMultipleSegmentsIndex(documents, index, testCase.numSegments)
			if err != nil {
				cleanUp(t, nameToIndex)
				t.Fatal(err)
			}

			query = copySearchRequest(originalRequest, nil)
			query.Sort = reqSort.Copy()
			query.AddKNNOperator(operator)
			query.Explain = true

			experimentalResult, err := index.Search(query)
			if err != nil {
				cleanUp(t, nameToIndex)
				t.Fatal(err)
			}
			if !finalHitsHaveValidIndex(experimentalResult.Hits, nameToIndex) {
				cleanUp(t, nameToIndex)
				t.Fatalf("test case #%d failed: expected experimental result hits to have valid `Index`", testCaseNum)
			}
			verifyResult(t, controlResult, experimentalResult, testCaseNum, false)
			cleanUp(t, nameToIndex)
		}
	}
}

// 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++ {
		err = batch.Index(strconv.Itoa(i), dataset[i])
		if err != nil {
			t.Fatal(err)
		}
	}

	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, err := index.Search(searchRequest)
	if err != nil {
		t.Fatal(err)
	}
	hitsMap := make(map[string]float64, 0)
	for _, hit := range hits.Hits {
		hitsMap[hit.ID] = (hit.Score)
	}

	searchRequest = NewSearchRequest(NewMatchNoneQuery())
	searchRequest.AddKNN("vector", queryVec, 3, 10.0)
	searchRequest.Fields = []string{"content", "vector"}

	hits, err = index.Search(searchRequest)
	if err != nil {
		t.Fatal(err)
	}
	hitsMap2 := make(map[string]float64, 0)
	for _, hit := range hits.Hits {
		hitsMap2[hit.ID] = (hit.Score)
	}

	for _, hit := range hits.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)
	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 < 10; 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++ {
		err = batch.Index(strconv.Itoa(i), dataset[i])
		if err != nil {
			t.Fatal(err)
		}
	}

	err = index.Batch(batch)
	if err != nil {
		t.Fatal(err)
	}

	termQuery := query.NewTermQuery("2")

	searchRequest := NewSearchRequest(termQuery)
	searchRequest.AddKNN("vector", getRandomVector(), 3, 2.0)
	searchRequest.AddKNN("vector", getRandomVector(), 2, 1.5)
	searchRequest.Fields = []string{"content", "vector"}

	// Conjunction
	searchRequest.AddKNNOperator(knnOperatorAnd)
	conjunction, _, _, err := createKNNQuery(searchRequest, nil)
	if err != nil {
		t.Fatalf("unexpected error for AND knn operator")
	}

	conj, ok := conjunction.(*query.DisjunctionQuery)
	if !ok {
		t.Fatalf("expected disjunction query")
	}

	if len(conj.Disjuncts) != 2 {
		t.Fatalf("expected 2 disjuncts")
	}

	// Disjunction
	searchRequest.AddKNNOperator(knnOperatorOr)
	disjunction, _, _, err := createKNNQuery(searchRequest, nil)
	if err != nil {
		t.Fatalf("unexpected error for OR knn operator")
	}

	disj, ok := disjunction.(*query.DisjunctionQuery)
	if !ok {
		t.Fatalf("expected disjunction query")
	}

	if len(disj.Disjuncts) != 2 {
		t.Fatalf("expected 2 disjuncts")
	}

	// Incorrect operator.
	searchRequest.AddKNNOperator("bs_op")
	searchRequest.Query, _, _, err = createKNNQuery(searchRequest, nil)
	if err == nil {
		t.Fatalf("expected error for incorrect knn operator")
	}
}

func TestKNNFiltering(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{}, 0)

	// Indexing just a few docs to populate index.
	for i := 0; i < 10; i++ {
		dataset = append(dataset, map[string]interface{}{
			"type":    "vectorStuff",
			"content": strconv.Itoa(i + 1000),
			"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++ {
		// the id of term "i" is (i-1000)
		err = batch.Index(strconv.Itoa(i), dataset[i])
		if err != nil {
			t.Fatal(err)
		}
	}

	err = index.Batch(batch)
	if err != nil {
		t.Fatal(err)
	}

	termQuery := query.NewTermQuery("1004")
	filterRequest := NewSearchRequest(termQuery)
	filteredHits, err := index.Search(filterRequest)
	if err != nil {
		t.Fatal(err)
	}
	filteredDocIDs := make(map[string]struct{})
	for _, match := range filteredHits.Hits {
		filteredDocIDs[match.ID] = struct{}{}
	}

	searchRequest := NewSearchRequest(NewMatchNoneQuery())
	searchRequest.AddKNNWithFilter("vector", getRandomVector(), 3, 2.0, termQuery)
	searchRequest.Fields = []string{"content", "vector"}

	res, err := index.Search(searchRequest)
	if err != nil {
		t.Fatal(err)
	}
	// check if any of the returned results are not part of the filtered hits.
	for _, match := range res.Hits {
		if _, exists := filteredDocIDs[match.ID]; !exists {
			t.Errorf("returned result not present in filtered hits")
		}
	}

	// No results should be returned with a match_none filter.
	searchRequest = NewSearchRequest(NewMatchNoneQuery())
	searchRequest.AddKNNWithFilter("vector", getRandomVector(), 3, 2.0,
		NewMatchNoneQuery())
	res, err = index.Search(searchRequest)
	if err != nil {
		t.Fatal(err)
	}
	if len(res.Hits) != 0 {
		t.Errorf("match none filter should return no hits")
	}

	// Testing with a disjunction query.

	termQuery = query.NewTermQuery("1003")
	termQuery2 := query.NewTermQuery("1005")
	disjQuery := query.NewDisjunctionQuery([]query.Query{termQuery, termQuery2})
	filterRequest = NewSearchRequest(disjQuery)
	filteredHits, err = index.Search(filterRequest)
	if err != nil {
		t.Fatal(err)
	}
	filteredDocIDs = make(map[string]struct{})
	for _, match := range filteredHits.Hits {
		filteredDocIDs[match.ID] = struct{}{}
	}

	searchRequest = NewSearchRequest(NewMatchNoneQuery())
	searchRequest.AddKNNWithFilter("vector", getRandomVector(), 3, 2.0, disjQuery)
	searchRequest.Fields = []string{"content", "vector"}

	res, err = index.Search(searchRequest)
	if err != nil {
		t.Fatal(err)
	}

	for _, match := range res.Hits {
		if _, exists := filteredDocIDs[match.ID]; !exists {
			t.Errorf("returned result not present in filtered hits")
		}
	}
}

// -----------------------------------------------------------------------------
// Test nested vectors

func TestNestedVectors(t *testing.T) {
	tmpIndexPath := createTmpIndexPath(t)
	defer cleanupTmpIndexPath(t, tmpIndexPath)

	const dims = 3
	const k = 1 // one nearest neighbor
	const vecFieldName = "vecData"

	dataset := map[string]map[string]interface{}{ // docID -> Doc
		"doc1": {
			vecFieldName: []float32{100, 100, 100},
		},
		"doc2": {
			vecFieldName: [][]float32{{0, 0, 0}, {1000, 1000, 1000}},
		},
	}

	// Index mapping
	indexMapping := NewIndexMapping()
	vm := mapping.NewVectorFieldMapping()
	vm.Dims = dims
	vm.Similarity = "l2_norm"
	indexMapping.DefaultMapping.AddFieldMappingsAt(vecFieldName, vm)

	// Create index and upload documents
	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 docID, doc := range dataset {
		err = batch.Index(docID, doc)
		if err != nil {
			t.Fatal(err)
		}
	}

	err = index.Batch(batch)
	if err != nil {
		t.Fatal(err)
	}

	// Run searches

	tests := []struct {
		queryVec      []float32
		expectedDocID string
	}{
		{
			queryVec:      []float32{100, 100, 100},
			expectedDocID: "doc1",
		},
		{
			queryVec:      []float32{0, 0, 0},
			expectedDocID: "doc2",
		},
		{
			queryVec:      []float32{1000, 1000, 1000},
			expectedDocID: "doc2",
		},
	}

	for _, test := range tests {
		searchReq := NewSearchRequest(query.NewMatchNoneQuery())
		searchReq.AddKNNWithFilter(vecFieldName, test.queryVec, k, 1000,
			NewMatchAllQuery())

		res, err := index.Search(searchReq)
		if err != nil {
			t.Fatal(err)
		}

		if len(res.Hits) != 1 {
			t.Fatalf("expected 1 hit, got %d", len(res.Hits))
		}

		if res.Hits[0].ID != test.expectedDocID {
			t.Fatalf("expected docID %s, got %s", test.expectedDocID,
				res.Hits[0].ID)
		}
	}
}

// -----------------------------------------------------------------------------
// TestMultiVector tests the KNN functionality which handles duplicate
// vectors being matched within the same document. When a document has multiple vectors
// (via [[]] array of vectors or [{}] array of objects with vectors), the KNN
// searcher must pick the best scoring vector match for that document. This test covers these scenarios:
// - Single vector field (baseline)
// - [[]] style: array of vectors (same doc appears multiple times)
// - [{}] style: array of objects with vector field (chunks pattern)
func TestMultiVector(t *testing.T) {
	tmpIndexPath := createTmpIndexPath(t)
	defer cleanupTmpIndexPath(t, tmpIndexPath)

	// JSON documents covering merger scenarios:
	// - Single vector (baseline)
	// - [[]] style: array of vectors (same doc appears multiple times)
	// - [{}] style: array of objects with vector field (chunks pattern)
	docs := map[string]string{
		// Single vector - baseline
		"doc1": `{
			"vec": [10, 10, 10],
			"vecB": [100, 100, 100]
		}`,
		// [[]] style - array of 2 vectors
		"doc2": `{
			"vec": [[0, 0, 0], [500, 500, 500]],
			"vecB": [[900, 900, 900], [950, 950, 950], [975, 975, 975], [990, 990, 990]]
		}`,
		// [[]] style - array of 3 vectors
		"doc3": `{ 
			"vec": [[50, 50, 50], [200, 200, 200], [400, 400, 400]],
			"vecB": [[800, 800, 800], [850, 850, 850]]
		}`,
		// Single vector - baseline
		"doc4": `{
			"vec": [1000, 1000, 1000],
			"vecB": [1, 1, 1]
		}`,
		// [{}] style - array of objects with vector field (chunks pattern)
		"doc5": `{
			"chunks": [
				{"vec": [10, 10, 10], "text": "chunk1"},
				{"vec": [20, 20, 20], "text": "chunk2"},
				{"vec": [30, 30, 30], "text": "chunk3"},
				{"vec": [40, 40, 40], "text": "chunk4"}
			]
		}`,
		"doc6": `{
			"chunks": [
				{"vec": [[10, 10, 10],[20, 20, 20]], "text": "chunk1"},
				{"vec": [[30, 30, 30],[40, 40, 40]], "text": "chunk2"}
			]
		}`,
	}

	// Parse JSON documents
	dataset := make(map[string]map[string]interface{})
	for docID, jsonStr := range docs {
		var doc map[string]interface{}
		if err := json.Unmarshal([]byte(jsonStr), &doc); err != nil {
			t.Fatalf("failed to unmarshal %s: %v", docID, err)
		}
		dataset[docID] = doc
	}

	// Index mapping
	indexMapping := NewIndexMapping()

	vecMapping := mapping.NewVectorFieldMapping()
	vecMapping.Dims = 3
	vecMapping.Similarity = index.InnerProduct
	indexMapping.DefaultMapping.AddFieldMappingsAt("vec", vecMapping)
	indexMapping.DefaultMapping.AddFieldMappingsAt("vecB", vecMapping)

	// Nested chunks mapping for [{}] style
	chunksMapping := mapping.NewDocumentMapping()
	chunksMapping.AddFieldMappingsAt("vec", vecMapping)
	indexMapping.DefaultMapping.AddSubDocumentMapping("chunks", chunksMapping)

	// Create and populate index
	idx, err := New(tmpIndexPath, indexMapping)
	if err != nil {
		t.Fatal(err)
	}
	defer func() {
		if err := idx.Close(); err != nil {
			t.Fatal(err)
		}
	}()

	batch := idx.NewBatch()
	for docID, doc := range dataset {
		if err := batch.Index(docID, doc); err != nil {
			t.Fatal(err)
		}
	}
	if err := idx.Batch(batch); err != nil {
		t.Fatal(err)
	}

	// Test: Single KNN query - basic functionality
	t.Run("VecFieldSingle", func(t *testing.T) {
		searchReq := NewSearchRequest(query.NewMatchNoneQuery())
		searchReq.AddKNN("vec", []float32{1, 1, 1}, 20, 1.0)
		res, err := idx.Search(searchReq)
		if err != nil {
			t.Fatal(err)
		}
		// Inner product: score = sum(query_i * doc_i)
		// doc1 vec=[10,10,10]: 1*10*3 = 30
		// doc2 vec best is [500,500,500]: 1*500*3 = 1500
		// doc3 vec best is [400,400,400]: 1*400*3 = 1200
		// doc4 vec=[1000,1000,1000]: 1*1000*3 = 3000
		expectedResult := []struct {
			docID         string
			expectedScore float64
		}{
			{docID: "doc4", expectedScore: 3000},
			{docID: "doc2", expectedScore: 1500},
			{docID: "doc3", expectedScore: 1200},
			{docID: "doc1", expectedScore: 30},
		}

		if len(res.Hits) != len(expectedResult) {
			t.Fatalf("expected %d hits, got %d", len(expectedResult), len(res.Hits))
		}

		for i, expected := range expectedResult {
			if res.Hits[i].ID != expected.docID {
				t.Fatalf("at rank %d, expected docID %s, got %s", i+1, expected.docID, res.Hits[i].ID)
			}
			if res.Hits[i].Score != expected.expectedScore {
				t.Fatalf("at rank %d, expected score %v, got %v", i+1, expected.expectedScore, res.Hits[i].Score)
			}
		}
	})

	// Test: Single KNN query on vecB field
	t.Run("VecBFieldSingle", func(t *testing.T) {
		searchReq := NewSearchRequest(query.NewMatchNoneQuery())
		searchReq.AddKNN("vecB", []float32{1000, 1000, 1000}, 20, 1.0)
		res, err := idx.Search(searchReq)
		if err != nil {
			t.Fatal(err)
		}
		// Inner product: score = sum(query_i * doc_i) for each dimension
		// doc1: vecB=[100,100,100] -> 1000*100*3 = 300,000
		// doc2: vecB best is [990,990,990] -> 1000*990*3 = 2,970,000
		// doc3: vecB best is [850,850,850] -> 1000*850*3 = 2,550,000
		// doc4: vecB=[1,1,1] -> 1000*1*3 = 3,000
		expectedResult := []struct {
			docID         string
			expectedScore float64
		}{
			{docID: "doc2", expectedScore: 2970000},
			{docID: "doc3", expectedScore: 2550000},
			{docID: "doc1", expectedScore: 300000},
			{docID: "doc4", expectedScore: 3000},
		}

		if len(res.Hits) != len(expectedResult) {
			t.Fatalf("expected %d hits, got %d", len(expectedResult), len(res.Hits))
		}

		for i, expected := range expectedResult {
			if res.Hits[i].ID != expected.docID {
				t.Fatalf("at rank %d, expected docID %s, got %s", i+1, expected.docID, res.Hits[i].ID)
			}
			if res.Hits[i].Score != expected.expectedScore {
				t.Fatalf("at rank %d, expected score %v, got %v", i+1, expected.expectedScore, res.Hits[i].Score)
			}
		}
	})

	// Test: Single KNN query on nested chunks.vec field
	t.Run("ChunksVecFieldSingle", func(t *testing.T) {
		searchReq := NewSearchRequest(query.NewMatchNoneQuery())
		searchReq.AddKNN("chunks.vec", []float32{1, 1, 1}, 20, 1.0)
		searchReq.SortBy([]string{"_score", "docID"})
		res, err := idx.Search(searchReq)
		if err != nil {
			t.Fatal(err)
		}

		// Only doc5 and doc6 have chunks.vec
		// doc5 chunks: [10,10,10], [20,20,20], [30,30,30], [40,40,40]
		//   Best score: 1*40*3 = 120
		// doc6 chunks: [[10,10,10],[20,20,20]], [[30,30,30],[40,40,40]]
		//   Best score: 1*40*3 = 120
		if len(res.Hits) != 2 {
			t.Fatalf("expected 2 hits, got %d", len(res.Hits))
		}

		// Both should have score 120
		for _, hit := range res.Hits {
			if hit.ID != "doc5" && hit.ID != "doc6" {
				t.Fatalf("unexpected docID %s, expected doc5 or doc6", hit.ID)
			}
			if hit.Score != 120 {
				t.Fatalf("for %s, expected score 120, got %v", hit.ID, hit.Score)
			}
		}
	})
}

// TestMultiVectorCosineNormalization verifies that multi-vector fields are
// normalized correctly with cosine similarity. Each sub-vector in a multi-vector
// should be independently normalized, producing correct similarity scores.
func TestMultiVectorCosineNormalization(t *testing.T) {
	tmpIndexPath := createTmpIndexPath(t)
	defer cleanupTmpIndexPath(t, tmpIndexPath)

	const dims = 3

	// Create index with cosine similarity
	indexMapping := NewIndexMapping()
	vecFieldMapping := mapping.NewVectorFieldMapping()
	vecFieldMapping.Dims = dims
	vecFieldMapping.Similarity = index.CosineSimilarity

	// Single-vector field
	indexMapping.DefaultMapping.AddFieldMappingsAt("vec", vecFieldMapping)
	// Multi-vector field
	indexMapping.DefaultMapping.AddFieldMappingsAt("multi_vec", vecFieldMapping)

	idx, err := New(tmpIndexPath, indexMapping)
	if err != nil {
		t.Fatal(err)
	}
	defer func() {
		err := idx.Close()
		if err != nil {
			t.Fatal(err)
		}
	}()

	docsString := []string{
		`{"vec": [3, 0, 0]}`,
		`{"vec": [0, 4, 0]}`,
		`{"multi_vec": [[3, 0, 0], [0, 4, 0]]}`,
	}

	for i, docStr := range docsString {
		var doc map[string]interface{}
		err = json.Unmarshal([]byte(docStr), &doc)
		if err != nil {
			t.Fatal(err)
		}
		err = idx.Index(fmt.Sprintf("doc%d", i+1), doc)
		if err != nil {
			t.Fatal(err)
		}
	}

	// Query for X direction [1,0,0]
	searchReq := NewSearchRequest(query.NewMatchNoneQuery())
	searchReq.AddKNN("vec", []float32{1, 0, 0}, 3, 1.0)
	res, err := idx.Search(searchReq)
	if err != nil {
		t.Fatal(err)
	}
	if len(res.Hits) != 2 {
		t.Fatalf("expected 2 hits, got %d", len(res.Hits))
	}
	// Hit 1 should be doc1 with score 1.0 (perfect match)
	if res.Hits[0].ID != "doc1" {
		t.Fatalf("expected doc1 as first hit, got %s", res.Hits[0].ID)
	}
	if math.Abs(float64(res.Hits[0].Score-1.0)) > 1e-6 {
		t.Fatalf("expected score 1.0, got %f", res.Hits[0].Score)
	}
	// Hit 2 should be doc2 with a score of 0.0 (orthogonal)
	if res.Hits[1].ID != "doc2" {
		t.Fatalf("expected doc2 as second hit, got %s", res.Hits[1].ID)
	}
	if math.Abs(float64(res.Hits[1].Score-0.0)) > 1e-6 {
		t.Fatalf("expected score 0.0, got %f", res.Hits[1].Score)
	}

	// Query for Y direction [0,1,0]
	searchReq = NewSearchRequest(query.NewMatchNoneQuery())
	searchReq.AddKNN("vec", []float32{0, 1, 0}, 3, 1.0)
	res, err = idx.Search(searchReq)
	if err != nil {
		t.Fatal(err)
	}
	if len(res.Hits) != 2 {
		t.Fatalf("expected 2 hits, got %d", len(res.Hits))
	}
	// Hit 1 should be doc2 with score 1.0 (perfect match)
	if res.Hits[0].ID != "doc2" {
		t.Fatalf("expected doc2 as first hit, got %s", res.Hits[0].ID)
	}
	if math.Abs(float64(res.Hits[0].Score-1.0)) > 1e-6 {
		t.Fatalf("expected score 1.0, got %f", res.Hits[0].Score)
	}
	// Hit 2 should be doc1 with a score of 0.0 (orthogonal)
	if res.Hits[1].ID != "doc1" {
		t.Fatalf("expected doc1 as second hit, got %s", res.Hits[1].ID)
	}
	if math.Abs(float64(res.Hits[1].Score-0.0)) > 1e-6 {
		t.Fatalf("expected score 0.0, got %f", res.Hits[1].Score)
	}

	// Now test querying the nested multi-vector field
	searchReq = NewSearchRequest(query.NewMatchNoneQuery())
	searchReq.AddKNN("multi_vec", []float32{1, 0, 0}, 3, 1.0)
	res, err = idx.Search(searchReq)
	if err != nil {
		t.Fatal(err)
	}
	if len(res.Hits) != 1 {
		t.Fatalf("expected 1 hit, got %d", len(res.Hits))
	}
	// Hit should be doc3 with score 1.0 (perfect match on first sub-vector)
	if res.Hits[0].ID != "doc3" {
		t.Fatalf("expected doc3 as first hit, got %s", res.Hits[0].ID)
	}
	if math.Abs(float64(res.Hits[0].Score-1.0)) > 1e-6 {
		t.Fatalf("expected score 1.0, got %f", res.Hits[0].Score)
	}
	// Query for Y direction [0,1,0] on nested field
	searchReq = NewSearchRequest(query.NewMatchNoneQuery())
	searchReq.AddKNN("multi_vec", []float32{0, 1, 0}, 3, 1.0)
	res, err = idx.Search(searchReq)
	if err != nil {
		t.Fatal(err)
	}
	if len(res.Hits) != 1 {
		t.Fatalf("expected 1 hit, got %d", len(res.Hits))
	}
	// Hit should be doc3 with score 1.0 (perfect match on second sub-vector)
	if res.Hits[0].ID != "doc3" {
		t.Fatalf("expected doc3 as first hit, got %s", res.Hits[0].ID)
	}
	if math.Abs(float64(res.Hits[0].Score-1.0)) > 1e-6 {
		t.Fatalf("expected score 1.0, got %f", res.Hits[0].Score)
	}
}

func TestNumVecsStat(t *testing.T) {

	dataset, _, err := readDatasetAndQueries(testInputCompressedFile)
	if err != nil {
		t.Fatal(err)
	}
	documents := makeDatasetIntoDocuments(dataset)

	indexMapping := NewIndexMapping()

	contentFieldMapping := NewTextFieldMapping()
	contentFieldMapping.Analyzer = en.AnalyzerName
	indexMapping.DefaultMapping.AddFieldMappingsAt("content", contentFieldMapping)

	vecFieldMapping1 := mapping.NewVectorFieldMapping()
	vecFieldMapping1.Dims = testDatasetDims
	vecFieldMapping1.Similarity = index.EuclideanDistance
	indexMapping.DefaultMapping.AddFieldMappingsAt("vector", vecFieldMapping1)

	tmpIndexPath := createTmpIndexPath(t)
	index, err := New(tmpIndexPath, indexMapping)
	if err != nil {
		t.Fatal(err)
	}
	defer func() {
		err := index.Close()
		if err != nil {
			t.Fatal(err)
		}
	}()

	for i := 0; i < 10; i++ {
		batch := index.NewBatch()
		for j := 0; j < 3; j++ {
			for k := 0; k < 10; k++ {
				err := batch.Index(fmt.Sprintf("%d", i*30+j*10+k), documents[j*10+k])
				if err != nil {
					t.Fatal(err)
				}
			}
		}
		err = index.Batch(batch)
		if err != nil {
			t.Fatal(err)
		}
	}

	statsMap := index.StatsMap()

	if indexStats, exists := statsMap["index"]; exists {
		if indexStatsMap, ok := indexStats.(map[string]interface{}); ok {
			v1, ok := indexStatsMap["field:vector:num_vectors"].(uint64)
			if !ok || v1 != uint64(300) {
				t.Fatalf("mismatch in the number of vectors, expected 300, got %d", indexStatsMap["field:vector:num_vectors"])
			}
		}
	}
}

func TestIndexUpdateVector(t *testing.T) {
	tmpIndexPath := createTmpIndexPath(t)
	defer cleanupTmpIndexPath(t, tmpIndexPath)

	indexMappingBefore := mapping.NewIndexMapping()
	indexMappingBefore.TypeMapping = map[string]*mapping.DocumentMapping{}
	indexMappingBefore.DefaultMapping = &mapping.DocumentMapping{
		Enabled: true,
		Dynamic: false,
		Properties: map[string]*mapping.DocumentMapping{
			"a": {
				Enabled:    true,
				Dynamic:    false,
				Properties: map[string]*mapping.DocumentMapping{},
				Fields: []*mapping.FieldMapping{
					{
						Type:                    "vector",
						Index:                   true,
						Dims:                    4,
						Similarity:              "l2_norm",
						VectorIndexOptimizedFor: "latency",
					},
				},
			},
			"b": {
				Enabled:    true,
				Dynamic:    false,
				Properties: map[string]*mapping.DocumentMapping{},
				Fields: []*mapping.FieldMapping{
					{
						Type:                    "vector",
						Index:                   true,
						Dims:                    4,
						Similarity:              "l2_norm",
						VectorIndexOptimizedFor: "latency",
					},
				},
			},
			"c": {
				Enabled:    true,
				Dynamic:    false,
				Properties: map[string]*mapping.DocumentMapping{},
				Fields: []*mapping.FieldMapping{
					{
						Type:                    "vector_base64",
						Index:                   true,
						Dims:                    4,
						Similarity:              "l2_norm",
						VectorIndexOptimizedFor: "latency",
					},
				},
			},
			"d": {
				Enabled:    true,
				Dynamic:    false,
				Properties: map[string]*mapping.DocumentMapping{},
				Fields: []*mapping.FieldMapping{
					{
						Type:                    "vector_base64",
						Index:                   true,
						Dims:                    4,
						Similarity:              "l2_norm",
						VectorIndexOptimizedFor: "latency",
					},
				},
			},
		},
		Fields: []*mapping.FieldMapping{},
	}
	indexMappingBefore.IndexDynamic = false
	indexMappingBefore.StoreDynamic = false
	indexMappingBefore.DocValuesDynamic = false

	index, err := New(tmpIndexPath, indexMappingBefore)
	if err != nil {
		t.Fatal(err)
	}
	doc1 := map[string]interface{}{"a": []float32{0.32894259691238403, 0.6973215341567993, 0.6835201978683472, 0.38296082615852356}, "b": []float32{0.32894259691238403, 0.6973215341567993, 0.6835201978683472, 0.38296082615852356}, "c": "L5MOPw7NID5SQMU9pHUoPw==", "d": "L5MOPw7NID5SQMU9pHUoPw=="}
	doc2 := map[string]interface{}{"a": []float32{0.0018692062003538013, 0.41076546907424927, 0.5675257444381714, 0.45832985639572144}, "b": []float32{0.0018692062003538013, 0.41076546907424927, 0.5675257444381714, 0.45832985639572144}, "c": "czloP94ZCD71ldY+GbAOPw==", "d": "czloP94ZCD71ldY+GbAOPw=="}
	doc3 := map[string]interface{}{"a": []float32{0.7853356599807739, 0.6904757618904114, 0.5643226504325867, 0.682637631893158}, "b": []float32{0.7853356599807739, 0.6904757618904114, 0.5643226504325867, 0.682637631893158}, "c": "Chh6P2lOqT47mjg/0odlPg==", "d": "Chh6P2lOqT47mjg/0odlPg=="}
	batch := index.NewBatch()
	err = batch.Index("001", doc1)
	if err != nil {
		t.Fatal(err)
	}
	err = batch.Index("002", doc2)
	if err != nil {
		t.Fatal(err)
	}
	err = batch.Index("003", doc3)
	if err != nil {
		t.Fatal(err)
	}
	err = index.Batch(batch)
	if err != nil {
		t.Fatal(err)
	}
	err = index.Close()
	if err != nil {
		t.Fatal(err)
	}

	indexMappingAfter := mapping.NewIndexMapping()
	indexMappingAfter.TypeMapping = map[string]*mapping.DocumentMapping{}
	indexMappingAfter.DefaultMapping = &mapping.DocumentMapping{
		Enabled: true,
		Dynamic: false,
		Properties: map[string]*mapping.DocumentMapping{
			"a": {
				Enabled:    true,
				Dynamic:    false,
				Properties: map[string]*mapping.DocumentMapping{},
				Fields: []*mapping.FieldMapping{
					{
						Type:                    "vector",
						Index:                   true,
						Dims:                    4,
						Similarity:              "l2_norm",
						VectorIndexOptimizedFor: "latency",
					},
				},
			},
			"c": {
				Enabled:    true,
				Dynamic:    false,
				Properties: map[string]*mapping.DocumentMapping{},
				Fields: []*mapping.FieldMapping{
					{
						Type:                    "vector_base64",
						Index:                   true,
						Dims:                    4,
						Similarity:              "l2_norm",
						VectorIndexOptimizedFor: "latency",
					},
				},
			},
			"d": {
				Enabled:    true,
				Dynamic:    false,
				Properties: map[string]*mapping.DocumentMapping{},
				Fields: []*mapping.FieldMapping{
					{
						Type:                    "vector_base64",
						Index:                   false,
						Dims:                    4,
						Similarity:              "l2_norm",
						VectorIndexOptimizedFor: "latency",
					},
				},
			},
		},
		Fields: []*mapping.FieldMapping{},
	}
	indexMappingAfter.IndexDynamic = false
	indexMappingAfter.StoreDynamic = false
	indexMappingAfter.DocValuesDynamic = false

	mappingString, err := json.Marshal(indexMappingAfter)
	if err != nil {
		t.Fatal(err)
	}
	config := map[string]interface{}{
		"updated_mapping": string(mappingString),
	}

	index, err = OpenUsing(tmpIndexPath, config)
	if err != nil {
		t.Fatal(err)
	}

	q1 := NewSearchRequest(NewMatchNoneQuery())
	q1.AddKNN("a", []float32{1, 2, 3, 4}, 3, 1.0)
	res1, err := index.Search(q1)
	if err != nil {
		t.Fatal(err)
	}
	if len(res1.Hits) != 3 {
		t.Fatalf("Expected 3 hits, got %d", len(res1.Hits))
	}
	q2 := NewSearchRequest(NewMatchNoneQuery())
	q2.AddKNN("b", []float32{1, 2, 3, 4}, 3, 1.0)
	res2, err := index.Search(q2)
	if err != nil {
		t.Fatal(err)
	}
	if len(res2.Hits) != 0 {
		t.Fatalf("Expected 0 hits, got %d", len(res2.Hits))
	}
	q3 := NewSearchRequest(NewMatchNoneQuery())
	q3.AddKNN("c", []float32{1, 2, 3, 4}, 3, 1.0)
	res3, err := index.Search(q3)
	if err != nil {
		t.Fatal(err)
	}
	if len(res3.Hits) != 3 {
		t.Fatalf("Expected 3 hits, got %d", len(res3.Hits))
	}
	q4 := NewSearchRequest(NewMatchNoneQuery())
	q4.AddKNN("d", []float32{1, 2, 3, 4}, 3, 1.0)
	res4, err := index.Search(q4)
	if err != nil {
		t.Fatal(err)
	}
	if len(res4.Hits) != 0 {
		t.Fatalf("Expected 0 hits, got %d", len(res4.Hits))
	}
}

func TestIndexInsightsTermFrequencies(t *testing.T) {
	tmpIndexPath := createTmpIndexPath(t)
	defer cleanupTmpIndexPath(t, tmpIndexPath)

	mp := mapping.NewIndexMapping()
	textMapping := mapping.NewTextFieldMapping()
	textMapping.Analyzer = "en"
	mp.DefaultMapping.AddFieldMappingsAt("text", textMapping)

	idx, err := New(tmpIndexPath, mp)
	if err != nil {
		t.Fatal(err)
	}
	defer func() {
		err = idx.Close()
		if err != nil {
			t.Fatal(err)
		}
	}()

	data := []map[string]string{
		{
			"id":   "one",
			"text": "She sells sea shells by the sea shore",
		},
		{
			"id":   "two",
			"text": "The quick brown fox jumps over the lazy dog",
		},
		{
			"id":   "three",
			"text": "She sold sea shells to the person with the dog",
		},
		{
			"id":   "four",
			"text": "But there are a lot of dogs on the beach",
		},
		{
			"id":   "five",
			"text": "To hell with the foxes",
		},
		{
			"id":   "six",
			"text": "What about the dogs",
		},
		{
			"id":   "seven",
			"text": "Dogs are OK, foxes are not",
		},
	}

	expectTermFreqs := []index.TermFreq{
		{Term: "dog", Frequency: 5},
		{Term: "fox", Frequency: 3},
		{Term: "sea", Frequency: 2},
		{Term: "shell", Frequency: 2},
		{Term: "beach", Frequency: 1},
	}

	for _, d := range data {
		err = idx.Index(d["id"], d)
		if err != nil {
			t.Errorf("Error updating index: %v", err)
		}
	}

	insightsIdx, ok := idx.(InsightsIndex)
	if !ok {
		t.Fatal("index does not support insights")
	}

	termFreqs, err := insightsIdx.TermFrequencies("text", 5, true)
	if err != nil {
		t.Fatal(err)
	}

	if !reflect.DeepEqual(termFreqs, expectTermFreqs) {
		t.Fatalf("term freqs do not match: got: %v, expected: %v", termFreqs, expectTermFreqs)
	}
}

func TestIndexInsightsCentroidCardinalities(t *testing.T) {
	tmpIndexPath := createTmpIndexPath(t)
	defer cleanupTmpIndexPath(t, tmpIndexPath)

	vectorDims := 5

	mp := mapping.NewIndexMapping()
	vecFieldMapping := mapping.NewVectorFieldMapping()
	vecFieldMapping.Dims = vectorDims
	vecFieldMapping.Similarity = index.CosineSimilarity
	mp.DefaultMapping.AddFieldMappingsAt("vec", vecFieldMapping)

	idx, err := New(tmpIndexPath, mp)
	if err != nil {
		t.Fatal(err)
	}
	defer func() {
		err = idx.Close()
		if err != nil {
			t.Fatal(err)
		}
	}()

	rand.Seed(time.Now().UnixNano())
	min, max := float32(-10.0), float32(10.0)
	genRandomVector := func() []float32 {
		vec := make([]float32, vectorDims)
		for i := range vec {
			vec[i] = min + rand.Float32()*(max-min)
		}
		return vec
	}

	batch := idx.NewBatch()
	for i := 1; i <= 50000; i++ {
		if err = batch.Index(fmt.Sprintf("doc-%d", i), map[string]interface{}{
			"vec": genRandomVector(),
		}); err != nil {
			t.Fatalf("error indexing doc: %v", err)
		}

		if i%200 == 0 {
			err = idx.Batch(batch)
			if err != nil {
				t.Fatalf("Error adding batch to index: %v", err)
			}
			batch = idx.NewBatch()
		}
	}

	if batch.Size() > 0 {
		// In case doc count is not a multiple of 200, we need to add the final batch
		err = idx.Batch(batch)
		if err != nil {
			t.Errorf("Error adding final batch to index: %v", err)
		}
	}

	insightsIdx, ok := idx.(InsightsIndex)
	if !ok {
		t.Fatal("index does not support insights")
	}

	centroids, err := insightsIdx.CentroidCardinalities("vec", 5, true)
	if err != nil {
		t.Fatal(err)
	}

	if len(centroids) != 5 {
		t.Fatalf("expected 5 centroids, got %d", len(centroids))
	}

	for _, entry := range centroids {
		if len(entry.Index) == 0 {
			t.Fatal("expected index name for each centroid")
		}
	}
}

func TestHierarchicalNestedVectorSearch(t *testing.T) {
	tmpIndexPath := createTmpIndexPath(t)
	defer cleanupTmpIndexPath(t, tmpIndexPath)

	dataset := `
	[
		{
			"id": "doc1",
			"items": [
				{
					"description": "I like trains",
					"embedding_vector": [
						1,
						0,
						0
					],
					"type": "transport"
				},
				{
					"description": "I love pizza",
					"embedding_vector": [
						0,
						1,
						0
					],
					"type": "food"
				}
			]
		},
		{
			"id": "doc2",
			"items": [
				{
					"description": "I go to school by bus",
					"embedding_vector": [
						0.9,
						0.1,
						0
					],
					"type": "transport"
				},
				{
					"description": "Sushi is delicious",
					"embedding_vector": [
						0,
						1,
						0
					],
					"type": "food"
				}
			]
		},
		{
			"id": "doc3",
			"items": [
				{
					"description": "Hamburgers are tasty",
					"embedding_vector": [
						0,
						0.8,
						0.2
					],
					"type": "food"
				},
				{
					"description": "I enjoy biking",
					"embedding_vector": [
						0.7,
						0,
						0.3
					],
					"type": "transport"
				}
			]
		}
	]`
	var documents []map[string]interface{}
	err := json.Unmarshal([]byte(dataset), &documents)
	if err != nil {
		t.Fatalf("failed to unmarshal dataset: %v", err)
	}
	indexMapping := NewIndexMapping()
	vecFieldMapping := mapping.NewVectorFieldMapping()
	vecFieldMapping.Dims = 3
	vecFieldMapping.Similarity = index.CosineSimilarity

	typeMapping := mapping.NewTextFieldMapping()
	typeMapping.Analyzer = keyword.Name

	descMapping := mapping.NewTextFieldMapping()
	descMapping.Analyzer = en.AnalyzerName

	// items is NOT nested
	itemsMapping := mapping.NewDocumentMapping()
	itemsMapping.AddFieldMappingsAt("embedding_vector", vecFieldMapping)
	itemsMapping.AddFieldMappingsAt("type", typeMapping)
	itemsMapping.AddFieldMappingsAt("description", descMapping)

	indexMapping.DefaultMapping.AddSubDocumentMapping("items", itemsMapping)
	idx, err := New(tmpIndexPath, indexMapping)
	if err != nil {
		t.Fatalf("failed to create index: %v", err)
	}
	defer func() {
		if err := idx.Close(); err != nil {
			t.Fatalf("failed to close index: %v", err)
		}
	}()

	batch := idx.NewBatch()
	for _, doc := range documents {
		err := batch.Index(doc["id"].(string), doc)
		if err != nil {
			t.Fatalf("failed to index document %s: %v", doc["id"], err)
		}
	}
	err = idx.Batch(batch)
	if err != nil {
		t.Fatalf("failed to batch index documents: %v", err)
	}

	// Plain vector search
	searchReq := NewSearchRequest(query.NewMatchNoneQuery())
	searchReq.AddKNN("items.embedding_vector", []float32{0, 1, 0}, 5, 1.0)
	searchReq.SortBy([]string{"-_score", "_id"})

	res, err := idx.Search(searchReq)
	if err != nil {
		t.Fatalf("failed to execute search: %v", err)
	}

	expectedOrder := []string{"doc1", "doc2", "doc3"}
	expectedScores := []float64{1.0, 1.0, 0.970}
	if len(res.Hits) != len(expectedOrder) {
		t.Fatalf("expected %d hits, got %d", len(expectedOrder), len(res.Hits))
	}
	for i, expectedID := range expectedOrder {
		if res.Hits[i].ID != expectedID {
			t.Fatalf("at rank %d, expected docID %s, got %s", i+1, expectedID, res.Hits[i].ID)
		}
		if math.Abs(res.Hits[i].Score-expectedScores[i]) > 0.01 {
			t.Fatalf("at rank %d, expected score %.3f, got %.3f", i+1, expectedScores[i], res.Hits[i].Score)
		}
	}

	// Filtered vector search - should match output of plain vector search in non-nested case
	filterQuery := NewTermQuery("transport")
	filterQuery.SetField("items.type")
	searchReq = NewSearchRequest(query.NewMatchNoneQuery())
	searchReq.AddKNNWithFilter("items.embedding_vector", []float32{0, 1, 0}, 5, 1.0, filterQuery)
	searchReq.SortBy([]string{"-_score", "_id"})
	res, err = idx.Search(searchReq)
	if err != nil {
		t.Fatalf("failed to execute filtered search: %v", err)
	}
	if len(res.Hits) != len(expectedOrder) {
		t.Fatalf("expected %d hits, got %d", len(expectedOrder), len(res.Hits))
	}
	for i, expectedID := range expectedOrder {
		if res.Hits[i].ID != expectedID {
			t.Fatalf("at rank %d, expected docID %s, got %s", i+1, expectedID, res.Hits[i].ID)
		}
		if math.Abs(res.Hits[i].Score-expectedScores[i]) > 0.01 {
			t.Fatalf("at rank %d, expected score %.3f, got %.3f", i+1, expectedScores[i], res.Hits[i].Score)
		}
	}

	// items IS nested
	nestedItemsMapping := mapping.NewNestedDocumentMapping()
	nestedItemsMapping.AddFieldMappingsAt("embedding_vector", vecFieldMapping)
	nestedItemsMapping.AddFieldMappingsAt("type", typeMapping)
	nestedItemsMapping.AddFieldMappingsAt("description", descMapping)

	indexMappingNested := NewIndexMapping()
	indexMappingNested.DefaultMapping.AddSubDocumentMapping("items", nestedItemsMapping)
	idxNested, err := New(tmpIndexPath+"_nested", indexMappingNested)
	if err != nil {
		t.Fatalf("failed to create nested index: %v", err)
	}
	defer func() {
		if err := idxNested.Close(); err != nil {
			t.Fatalf("failed to close nested index: %v", err)
		}
	}()

	batch = idxNested.NewBatch()
	for _, doc := range documents {
		err := batch.Index(doc["id"].(string), doc)
		if err != nil {
			t.Fatalf("failed to index document %s in nested index: %v", doc["id"], err)
		}
	}
	err = idxNested.Batch(batch)
	if err != nil {
		t.Fatalf("failed to batch index documents in nested index: %v", err)
	}
	// Plain vector search on nested index
	searchReq = NewSearchRequest(query.NewMatchNoneQuery())
	searchReq.AddKNN("items.embedding_vector", []float32{0, 1, 0}, 5, 1.0)
	searchReq.SortBy([]string{"-_score", "_id"})

	res, err = idxNested.Search(searchReq)
	if err != nil {
		t.Fatalf("failed to execute search on nested index: %v", err)
	}
	// Exact same behavior as non-nested in this case
	if len(res.Hits) != len(expectedOrder) {
		t.Fatalf("expected %d hits, got %d", len(expectedOrder), len(res.Hits))
	}
	for i, expectedID := range expectedOrder {
		if res.Hits[i].ID != expectedID {
			t.Fatalf("at rank %d, expected docID %s, got %s", i+1, expectedID, res.Hits[i].ID)
		}
		if math.Abs(res.Hits[i].Score-expectedScores[i]) > 0.01 {
			t.Fatalf("at rank %d, expected score %.3f, got %.3f", i+1, expectedScores[i], res.Hits[i].Score)
		}
	}

	// Filtered vector search on nested index - should NOT match output of plain vector search in nested case
	filterQuery = NewTermQuery("transport")
	filterQuery.SetField("items.type")
	searchReq = NewSearchRequest(query.NewMatchNoneQuery())
	searchReq.AddKNNWithFilter("items.embedding_vector", []float32{0, 1, 0}, 5, 1.0, filterQuery)
	searchReq.SortBy([]string{"-_score", "_id"})
	res, err = idxNested.Search(searchReq)
	if err != nil {
		t.Fatalf("failed to execute filtered search on nested index: %v", err)
	}
	expectedNestedOrder := []string{"doc2", "doc1", "doc3"}
	expectedNestedScores := []float64{0.110, 0, 0}
	if len(res.Hits) != len(expectedNestedOrder) {
		t.Fatalf("expected %d hits, got %d", len(expectedNestedOrder), len(res.Hits))
	}
	for i, expectedID := range expectedNestedOrder {
		if res.Hits[i].ID != expectedID {
			t.Fatalf("at rank %d, expected docID %s, got %s", i+1, expectedID, res.Hits[i].ID)
		}
		if math.Abs(res.Hits[i].Score-expectedNestedScores[i]) > 0.01 {
			t.Fatalf("at rank %d, expected score %.3f, got %.3f", i+1, expectedNestedScores[i], res.Hits[i].Score)
		}
	}
}

func TestKNNNullParams(t *testing.T) {
	queries := []struct {
		query []byte
	}{
		{query: []byte(`{"knn": [{"field": "emb", "vector": [1, 2], "k": 3}]}`)},
		{query: []byte(`{"knn": [{"field": "emb", "params": null, "vector": [1, 2], "k": 3}]}`)},
		{query: []byte(`{"knn": [{"field": "emb","vector": [1, 2], "k": 3, "filter": null}]}`)},
		{query: []byte(`{"pre_search_data": null, "knn": [{"field": "emb", "vector": [1, 2], "k": 3}]}`)},
	}

	for _, q := range queries {
		var searchReq SearchRequest
		err := json.Unmarshal(q.query, &searchReq)
		if err != nil {
			t.Fatalf("failed to parse query: %v", err)
		}
		if len(searchReq.PreSearchData) > 0 {
			t.Fatalf("expected no pre_search_data for query: %s, got %v", q.query, searchReq.PreSearchData)
		}
		for _, req := range searchReq.KNN {
			if len(req.Params) > 0 {
				t.Fatalf("expected no params for query: %s, got %v", q.query, req.Params)
			}
			if req.FilterQuery != nil {
				t.Fatalf("expected no filter for query: %s, got %v", q.query, req.FilterQuery)
			}
		}
		marshalled, err := json.Marshal(searchReq)
		if err != nil {
			t.Fatalf("failed to marshal search request: %v", err)
		}
		var unmarshalled SearchRequest
		err = json.Unmarshal(marshalled, &unmarshalled)
		if err != nil {
			t.Fatalf("failed to unmarshal marshalled search request: %v", err)
		}
		if len(unmarshalled.PreSearchData) > 0 {
			t.Fatalf("expected no pre_search_data after marshal/unmarshal for query: %s, got %v", q.query, unmarshalled.PreSearchData)
		}
		for _, req := range unmarshalled.KNN {
			if len(req.Params) > 0 {
				t.Fatalf("expected no params after marshal/unmarshal for query: %s, got %v", q.query, req.Params)
			}
			if req.FilterQuery != nil {
				t.Fatalf("expected no filter after marshal/unmarshal for query: %s, got %v", q.query, req.FilterQuery)
			}
		}
	}
}

func TestVectorIndexExhaustion(t *testing.T) {
	for optimization := range index.SupportedVectorIndexOptimizations {
		t.Run(optimization, func(t *testing.T) {
			tmpIndexPath := createTmpIndexPath(t)
			defer cleanupTmpIndexPath(t, tmpIndexPath)

			const dims = 8

			vecFieldMapping := mapping.NewVectorFieldMapping()
			vecFieldMapping.Dims = dims
			vecFieldMapping.VectorIndexOptimizedFor = optimization

			indexMapping := NewIndexMapping()
			indexMapping.DefaultMapping.AddFieldMappingsAt("vec", vecFieldMapping)

			idx, err := New(tmpIndexPath, indexMapping)
			if err != nil {
				t.Fatal(err)
			}
			defer func() {
				err := idx.Close()
				if err != nil {
					t.Fatal(err)
				}
			}()

			docs := []struct {
				id  string
				vec []float32
			}{
				{"doc1", []float32{1, 0, 0, 0, 0, 0, 0, 0}},
				{"doc2", []float32{0, 1, 0, 0, 0, 0, 0, 0}},
				{"doc3", []float32{0, 0, 1, 0, 0, 0, 0, 0}},
			}

			batch := idx.NewBatch()
			for _, doc := range docs {
				err = batch.Index(doc.id, map[string]interface{}{
					"vec": doc.vec,
				})
				if err != nil {
					t.Fatal(err)
				}
			}
			err = idx.Batch(batch)
			if err != nil {
				t.Fatal(err)
			}

			searchReq := NewSearchRequest(query.NewMatchNoneQuery())
			searchReq.AddKNN("vec", []float32{1, 0, 0, 0, 0, 0, 0, 0}, 10, 1.0)
			res, err := idx.Search(searchReq)
			if err != nil {
				t.Fatal(err)
			}
			if len(res.Hits) != 3 {
				t.Fatalf("expected 3 hits, got %d", len(res.Hits))
			}
			if res.Hits[0].ID != "doc1" {
				t.Fatalf("expected doc1 as top hit, got %s", res.Hits[0].ID)
			}
		})
	}
}