diff --git a/examples/embeddings/simpleVector/main.go b/examples/embeddings/simpleVector/main.go
index 96b4b996..5270def4 100644
--- a/examples/embeddings/simpleVector/main.go
+++ b/examples/embeddings/simpleVector/main.go
@@ -30,7 +30,7 @@ func main() {
 		}
 	}
 
-	query := "Describe within a paragraph what is the purpose of the NATO Alliance."
+	query := "What is the purpose of the NATO Alliance?"
 	similarities, err := docsVectorIndex.Query(
 		context.Background(),
 		query,
@@ -83,7 +83,7 @@ func ingestData(docsVectorIndex *simplevectorindex.Index, openaiEmbedder index.E
 		return err
 	}
 
-	textSplitter := textsplitter.NewRecursiveCharacterTextSplitter(2000, 100)
+	textSplitter := textsplitter.NewRecursiveCharacterTextSplitter(1000, 20)
 
 	documentChunks := textSplitter.SplitDocuments(documents)
 
diff --git a/index/index.go b/index/index.go
index 3778ef4d..068e615c 100644
--- a/index/index.go
+++ b/index/index.go
@@ -3,7 +3,6 @@ package index
 import (
 	"context"
 	"errors"
-	"sort"
 
 	"github.com/henomis/lingoose/document"
 	"github.com/henomis/lingoose/embedder"
@@ -58,20 +57,6 @@ type Embedder interface {
 	Embed(ctx context.Context, texts []string) ([]embedder.Embedding, error)
 }
 
-func FilterSearchResults(searchResults SearchResults, topK int) SearchResults {
-	//sort by similarity score
-	sort.Slice(searchResults, func(i, j int) bool {
-		return searchResults[i].Score > searchResults[j].Score
-	})
-
-	maxTopK := topK
-	if maxTopK > len(searchResults) {
-		maxTopK = len(searchResults)
-	}
-
-	return searchResults[:maxTopK]
-}
-
 func DeepCopyMetadata(metadata types.Meta) types.Meta {
 	metadataCopy := make(types.Meta)
 	for k, v := range metadata {
diff --git a/index/pinecone/pinecone.go b/index/pinecone/pinecone.go
index 27137fd1..41405dd2 100644
--- a/index/pinecone/pinecone.go
+++ b/index/pinecone/pinecone.go
@@ -28,6 +28,7 @@ type Index struct {
 	namespace       string
 	embedder        index.Embedder
 	includeContent  bool
+	includeValues   bool
 	batchUpsertSize int
 
 	createIndex *CreateIndexOptions
@@ -44,6 +45,7 @@ type Options struct {
 	IndexName       string
 	Namespace       string
 	IncludeContent  bool
+	IncludeValues   bool
 	BatchUpsertSize *int
 	CreateIndex     *CreateIndexOptions
 }
@@ -65,6 +67,7 @@ func New(options Options, embedder index.Embedder) *Index {
 		embedder:        embedder,
 		namespace:       options.Namespace,
 		includeContent:  options.IncludeContent,
+		includeValues:   options.IncludeValues,
 		batchUpsertSize: batchUpsertSize,
 		createIndex:     options.CreateIndex,
 	}
@@ -165,9 +168,7 @@ func (p *Index) Search(ctx context.Context, values []float64, opts ...option.Opt
 		return nil, fmt.Errorf("%w: %w", index.ErrInternal, err)
 	}
 
-	searchResults := buildSearchResultsFromPineconeMatches(matches, p.includeContent)
-
-	return index.FilterSearchResults(searchResults, pineconeOptions.TopK), nil
+	return buildSearchResultsFromPineconeMatches(matches, p.includeContent), nil
 }
 
 func (p *Index) Query(ctx context.Context, query string, opts ...option.Option) (index.SearchResults, error) {
@@ -188,9 +189,7 @@ func (p *Index) Query(ctx context.Context, query string, opts ...option.Option)
 		return nil, fmt.Errorf("%w: %w", index.ErrInternal, err)
 	}
 
-	searchResults := buildSearchResultsFromPineconeMatches(matches, p.includeContent)
-
-	return index.FilterSearchResults(searchResults, pineconeOptions.TopK), nil
+	return buildSearchResultsFromPineconeMatches(matches, p.includeContent), nil
 }
 
 func (p *Index) query(ctx context.Context, query string, opts *option.Options) ([]pineconeresponse.QueryMatch, error) {
@@ -222,6 +221,7 @@ func (p *Index) similaritySearch(
 			TopK:            int32(opts.TopK),
 			Vector:          values,
 			IncludeMetadata: &includeMetadata,
+			IncludeValues:   &p.includeValues,
 			Namespace:       &p.namespace,
 			Filter:          opts.Filter.(map[string]string),
 		},
diff --git a/index/qdrant/qdrant.go b/index/qdrant/qdrant.go
index a39ccecc..81ccf6fe 100644
--- a/index/qdrant/qdrant.go
+++ b/index/qdrant/qdrant.go
@@ -25,6 +25,7 @@ type Index struct {
 	collectionName  string
 	embedder        index.Embedder
 	includeContent  bool
+	includeValues   bool
 	batchUpsertSize int
 
 	createCollection *CreateCollectionOptions
@@ -47,6 +48,7 @@ type CreateCollectionOptions struct {
 type Options struct {
 	CollectionName   string
 	IncludeContent   bool
+	IncludeValues    bool
 	BatchUpsertSize  *int
 	CreateCollection *CreateCollectionOptions
 }
@@ -67,6 +69,7 @@ func New(options Options, embedder index.Embedder) *Index {
 		collectionName:   options.CollectionName,
 		embedder:         embedder,
 		includeContent:   options.IncludeContent,
+		includeValues:    options.IncludeValues,
 		batchUpsertSize:  batchUpsertSize,
 		createCollection: options.CreateCollection,
 	}
@@ -150,9 +153,7 @@ func (q *Index) Search(ctx context.Context, values []float64, opts ...option.Opt
 		return nil, fmt.Errorf("%w: %w", index.ErrInternal, err)
 	}
 
-	searchResults := buildSearchResultsFromQdrantMatches(matches, q.includeContent)
-
-	return index.FilterSearchResults(searchResults, qdrantOptions.TopK), nil
+	return buildSearchResultsFromQdrantMatches(matches, q.includeContent), nil
 }
 
 func (q *Index) Query(ctx context.Context, query string, opts ...option.Option) (index.SearchResults, error) {
@@ -169,9 +170,7 @@ func (q *Index) Query(ctx context.Context, query string, opts ...option.Option)
 		return nil, fmt.Errorf("%w: %w", index.ErrInternal, err)
 	}
 
-	searchResults := buildSearchResultsFromQdrantMatches(matches, q.includeContent)
-
-	return index.FilterSearchResults(searchResults, qdrantOptions.TopK), nil
+	return buildSearchResultsFromQdrantMatches(matches, q.includeContent), nil
 }
 
 func (q *Index) similaritySearch(
@@ -192,6 +191,7 @@ func (q *Index) similaritySearch(
 			Limit:          opts.TopK,
 			Vector:         values,
 			WithPayload:    &includeMetadata,
+			WithVector:     &q.includeValues,
 			Filter:         opts.Filter.(qdrantrequest.Filter),
 		},
 		res,
diff --git a/index/simpleVectorIndex/simpleVectorIndex.go b/index/simpleVectorIndex/simpleVectorIndex.go
index 2ebad31a..203104b9 100644
--- a/index/simpleVectorIndex/simpleVectorIndex.go
+++ b/index/simpleVectorIndex/simpleVectorIndex.go
@@ -3,9 +3,11 @@ package simplevectorindex
 import (
 	"context"
 	"encoding/json"
+	"errors"
 	"fmt"
 	"math"
 	"os"
+	"sort"
 	"strings"
 
 	"github.com/google/uuid"
@@ -211,7 +213,10 @@ func (s *Index) similaritySearch(
 	opts *option.Options,
 ) (index.SearchResults, error) {
 	_ = ctx
-	scores := s.cosineSimilarityBatch(embedding)
+	scores, err := s.cosineSimilarityBatch(embedding)
+	if err != nil {
+		return nil, fmt.Errorf("%w: %w", index.ErrInternal, err)
+	}
 
 	searchResults := make([]index.SearchResult, len(scores))
 
@@ -230,33 +235,64 @@ func (s *Index) similaritySearch(
 		searchResults = opts.Filter.(FilterFn)(searchResults)
 	}
 
-	return index.FilterSearchResults(searchResults, opts.TopK), nil
+	return filterSearchResults(searchResults, opts.TopK), nil
 }
 
-func (s *Index) cosineSimilarity(a embedder.Embedding, b embedder.Embedding) float64 {
-	dotProduct := float64(0.0)
-	normA := float64(0.0)
-	normB := float64(0.0)
-
-	for i := 0; i < len(a); i++ {
-		dotProduct += a[i] * b[i]
-		normA += a[i] * a[i]
-		normB += b[i] * b[i]
+func (s *Index) cosineSimilarity(a []float64, b []float64) (cosine float64, err error) {
+	var count int
+	lengthA := len(a)
+	lengthB := len(b)
+	if lengthA > lengthB {
+		count = lengthA
+	} else {
+		count = lengthB
 	}
-
-	if normA == 0 || normB == 0 {
-		return float64(0.0)
+	sumA := 0.0
+	s1 := 0.0
+	s2 := 0.0
+	for k := 0; k < count; k++ {
+		if k >= lengthA {
+			s2 += math.Pow(b[k], 2)
+			continue
+		}
+		if k >= lengthB {
+			s1 += math.Pow(a[k], 2)
+			continue
+		}
+		sumA += a[k] * b[k]
+		s1 += math.Pow(a[k], 2)
+		s2 += math.Pow(b[k], 2)
 	}
-
-	return dotProduct / (math.Sqrt(normA) * math.Sqrt(normB))
+	if s1 == 0 || s2 == 0 {
+		return 0.0, errors.New("vectors should not be null (all zeros)")
+	}
+	return sumA / (math.Sqrt(s1) * math.Sqrt(s2)), nil
 }
 
-func (s *Index) cosineSimilarityBatch(a embedder.Embedding) []float64 {
+func (s *Index) cosineSimilarityBatch(a embedder.Embedding) ([]float64, error) {
+	var err error
 	scores := make([]float64, len(s.data))
 
 	for i := range s.data {
-		scores[i] = s.cosineSimilarity(a, s.data[i].Values)
+		scores[i], err = s.cosineSimilarity(a, s.data[i].Values)
+		if err != nil {
+			return nil, err
+		}
+	}
+
+	return scores, nil
+}
+
+func filterSearchResults(searchResults index.SearchResults, topK int) index.SearchResults {
+	//sort by similarity score
+	sort.Slice(searchResults, func(i, j int) bool {
+		return (1 - searchResults[i].Score) < (1 - searchResults[j].Score)
+	})
+
+	maxTopK := topK
+	if maxTopK > len(searchResults) {
+		maxTopK = len(searchResults)
 	}
 
-	return scores
+	return searchResults[:maxTopK]
 }