diff --git a/Package.swift b/Package.swift
index 22c20410d598d896db7a5771d83e22564be7dd69..be0eca8183d03a6856f4c9eb39eeae13e9f57bbc 100644
--- a/Package.swift
+++ b/Package.swift
@@ -32,7 +32,7 @@ let package = Package(
         .target(
             name: "SwiftNLP",
             dependencies: [
-                //"SwiftAnnoy",
+                "SwiftAnnoy",
                 "SwiftNLPGenericLLMMacros",
                 .product(name: "HNSWAlgorithm", package: "similarity-topology"),
                 .product(name: "HNSWEphemeral", package: "similarity-topology"),
diff --git a/Tests/SwiftNLPTests/AllMiniLM_sampleTest.swift b/Tests/SwiftNLPTests/AllMiniLM_sampleTest.swift
index 4c6be656c5bc3893176c81e54a12ee296eea1e88..0cdb0ce835f2609dd7c620552d1146316e5879e6 100644
--- a/Tests/SwiftNLPTests/AllMiniLM_sampleTest.swift
+++ b/Tests/SwiftNLPTests/AllMiniLM_sampleTest.swift
@@ -4,7 +4,7 @@ import XCTest
 import Darwin
 
 @testable import SwiftNLP
-// @testable import SwiftAnnoy
+@testable import SwiftAnnoy
 
 final class BERT_test: XCTestCase {
    
@@ -48,27 +48,28 @@ final class BERT_test: XCTestCase {
                     fatalError("Error occurred!")
                 }
             }
+            
+            let index = AnnoyIndex<Float>(itemLength: 384, metric: .euclidean)
+           
+            try? index.addItems(items: &database_embedding)
+            try? index.build(numTrees: 50)
+           
+            let results = index.getNNsForVector(vector: &query_embedding, neighbors: 8)
+           
+            if let finalresult = results {
+                let extractedIndeices = finalresult.indices
+                for index in extractedIndeices {
+                    if index < docs.count {
+                        print(docs[index])
+                    } else {
+                        print("Index \(index) out of range.")
+                    }
+                }
+            }
+            
+            print(results)
+            print(database_embedding)
         }
-       
-// //        let index = AnnoyIndex<Float>(itemLength: embedding_dim, metric: .euclidean)
-       
-// //        try? index.addItems(items: &database_embedding)
-// //        try? index.build(numTrees: 50)
-       
-// //        let results = index.getNNsForVector(vector: &query_embedding, neighbors: 8)
-       
-// // //        if let finalresult = results {
-// // //            let extractedIndeices = finalresult.indices
-// // //            for index in extractedIndeices {
-// // //                if index < docs.count {
-// // //                    print(docs[index])
-// // //                } else {
-// // //                    print("Index \(index) out of range.")
-// // //                }
-// // //            }
-// // //        }
-// //        print(results)
-//         print(database_embedding)
     }
 }
 #endif