diff --git a/cpp/unittest/faiss_wrapper/wrapper_test.cpp b/cpp/unittest/faiss_wrapper/wrapper_test.cpp
index b410d4a550..75a56fc19c 100644
--- a/cpp/unittest/faiss_wrapper/wrapper_test.cpp
+++ b/cpp/unittest/faiss_wrapper/wrapper_test.cpp
@@ -23,36 +23,72 @@ TEST(operand_test, Wrapper_Test) {
 
 TEST(build_test, Wrapper_Test) {
     // dimension of the vectors to index
-    int d = 64;
+    int d = 3;
+
+    // make a set of nt training vectors in the unit cube
+    size_t nt = 10000;
+
+    // a reasonable number of cetroids to index nb vectors
+    int ncentroids = 16;
+
+    std::random_device rd;
+    std::mt19937 gen(rd());
+
+    std::vector<float> xb;
+    std::vector<long> ids;
+
+    //prepare train data
+    std::uniform_real_distribution<> dis_xt(-1.0, 1.0);
+    std::vector<float> xt(nt * d);
+    for (size_t i = 0; i < nt * d; i++) {
+        xt[i] = dis_xt(gen);
+    }
+
+    //train the index
+    auto opd = std::make_shared<Operand>();
+    opd->index_type = "IVF16,Flat";
+    opd->d = d;
+    opd->ncent = ncentroids;
+    IndexBuilderPtr index_builder_1 = GetIndexBuilder(opd);
+    auto index_1 = index_builder_1->build_all(0, xb, ids, nt, xt);
+    ASSERT_TRUE(index_1 != nullptr);
 
     // size of the database we plan to index
     size_t nb = 100000;
 
-    // make a set of nt training vectors in the unit cube
-    size_t nt = 150000;
-
-    // a reasonable number of cetroids to index nb vectors
-    int ncentroids = 25;
-
-    srand48(35); // seed
-
-    std::vector<float> xb(nb * d);
-    for (size_t i = 0; i < nb * d; i++) {
-        xb[i] = drand48();
-    }
-
-    std::vector<long> ids(nb);
+    //prepare raw data
+    xb.resize(nb);
+    ids.resize(nb);
     for (size_t i = 0; i < nb; i++) {
-        ids[i] = drand48();
+        xb[i] = dis_xt(gen);
+        ids[i] = i;
+    }
+    index_1->add_with_ids(nb, xb.data(), ids.data());
+
+    //search in first quadrant
+    int nq = 1, k = 10;
+    std::vector<float> xq = {0.5, 0.5, 0.5};
+    float* result_dists = new float[k];
+    long* result_ids = new long[k];
+    index_1->search(nq, xq.data(), k, result_dists, result_ids);
+
+    for(int i = 0; i < k; i++) {
+        if(result_ids[i] < 0) {
+            ASSERT_TRUE(false);
+            break;
+        }
+
+        long id = result_ids[i];
+        std::cout << "No." << id << " [" << xb[id*3] << ", " << xb[id*3 + 1] << ", "
+            << xb[id*3 + 2] <<"] distance = " << result_dists[i] << std::endl;
+
+        //makesure result vector is in first quadrant
+        ASSERT_TRUE(xb[id*3] > 0.0);
+        ASSERT_TRUE(xb[id*3 + 1] > 0.0);
+        ASSERT_TRUE(xb[id*3 + 2] > 0.0);
     }
 
-    std::vector<float> xt(nt * d);
-    for (size_t i = 0; i < nt * d; i++) {
-        xt[i] = drand48();
-    }
-
-    auto opd = std::make_shared<Operand>();
-    IndexBuilderPtr index_builder_1 = GetIndexBuilder(opd);
-    auto index_1 = index_builder_1->build_all(nb, xb, ids, nt, xt);
+    delete[] result_dists;
+    delete[] result_ids;
 }