diff --git a/model/embedding.go b/model/embedding.go
index 30a463f..51e9787 100644
--- a/model/embedding.go
+++ b/model/embedding.go
@@ -59,18 +59,3 @@ func GetEmbeddingSafe(authToken string, text string) ([]float32, error) {
 		return embedding, nil
 	}
 }
-
-func GetNearestVectorIndex(target []float32, vectors [][]float32) int {
-	targetNorm := norm(target)
-
-	var res int
-	max := float32(-1.0)
-	for i, vector := range vectors {
-		similarity := cosineSimilarity(target, vector, targetNorm)
-		if similarity > max {
-			max = similarity
-			res = i
-		}
-	}
-	return res
-}
diff --git a/model/query_test.go b/model/query_test.go
index be5c5ba..4aef4ff 100644
--- a/model/query_test.go
+++ b/model/query_test.go
@@ -20,6 +20,7 @@ package model_test
 import (
 	"testing"
 
+	"github.com/casbin/casibase/model"
 	"github.com/casbin/casibase/object"
 	"github.com/casbin/casibase/proxy"
 	"github.com/sashabaranov/go-openai"
diff --git a/model/util.go b/model/util.go
index b04ffc3..d7db0f2 100644
--- a/model/util.go
+++ b/model/util.go
@@ -14,11 +14,7 @@
 
 package model
 
-import (
-	"math"
-
-	"github.com/pkoukk/tiktoken-go"
-)
+import "github.com/pkoukk/tiktoken-go"
 
 func GetTokenSize(model string, prompt string) (int, error) {
 	tkm, err := tiktoken.EncodingForModel(model)
@@ -30,32 +26,3 @@ func GetTokenSize(model string, prompt string) (int, error) {
 	res := len(token)
 	return res, nil
 }
-
-func cosineSimilarity(vec1, vec2 []float32, vec1Norm float32) float32 {
-	dotProduct := dot(vec1, vec2)
-	vec2Norm := norm(vec2)
-	if vec2Norm == 0 {
-		return 0.0
-	}
-	return dotProduct / (vec1Norm * vec2Norm)
-}
-
-func dot(vec1, vec2 []float32) float32 {
-	if len(vec1) != len(vec2) {
-		panic("Vector lengths do not match")
-	}
-
-	dotProduct := float32(0.0)
-	for i := range vec1 {
-		dotProduct += vec1[i] * vec2[i]
-	}
-	return dotProduct
-}
-
-func norm(vec []float32) float32 {
-	normSquared := float32(0.0)
-	for _, val := range vec {
-		normSquared += val * val
-	}
-	return float32(math.Sqrt(float64(normSquared)))
-}
diff --git a/object/vector_embedding.go b/object/vector_embedding.go
index d53b682..de018e5 100644
--- a/object/vector_embedding.go
+++ b/object/vector_embedding.go
@@ -146,6 +146,6 @@ func GetNearestVectorText(authToken string, owner string, question string) (stri
 		nVectors = append(nVectors, candidate.Data)
 	}
 
-	i := model.GetNearestVectorIndex(qVector, nVectors)
+	i := getNearestVectorIndex(qVector, nVectors)
 	return vectors[i].Text, nil
 }
diff --git a/object/vector_util.go b/object/vector_util.go
new file mode 100644
index 0000000..c785e78
--- /dev/null
+++ b/object/vector_util.go
@@ -0,0 +1,61 @@
+// Copyright 2023 The casbin Authors. All Rights Reserved.
+//
+// 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.
+
+package object
+
+import "math"
+
+func dot(vec1, vec2 []float32) float32 {
+	if len(vec1) != len(vec2) {
+		panic("Vector lengths do not match")
+	}
+
+	dotProduct := float32(0.0)
+	for i := range vec1 {
+		dotProduct += vec1[i] * vec2[i]
+	}
+	return dotProduct
+}
+
+func norm(vec []float32) float32 {
+	normSquared := float32(0.0)
+	for _, val := range vec {
+		normSquared += val * val
+	}
+	return float32(math.Sqrt(float64(normSquared)))
+}
+
+func cosineSimilarity(vec1, vec2 []float32, vec1Norm float32) float32 {
+	dotProduct := dot(vec1, vec2)
+	vec2Norm := norm(vec2)
+	if vec2Norm == 0 {
+		return 0.0
+	}
+	return dotProduct / (vec1Norm * vec2Norm)
+}
+
+func getNearestVectorIndex(target []float32, vectors [][]float32) int {
+	targetNorm := norm(target)
+
+	var res int
+	max := float32(-1.0)
+	for i, vector := range vectors {
+		similarity := cosineSimilarity(target, vector, targetNorm)
+		if similarity > max {
+			max = similarity
+			res = i
+		}
+	}
+	return res
+}