diff --git a/cpp/unittest/faiss_wrapper/wrapper_test.cpp b/cpp/unittest/faiss_wrapper/wrapper_test.cpp
index c1b4ff2cb0..67a6c3cde8 100644
--- a/cpp/unittest/faiss_wrapper/wrapper_test.cpp
+++ b/cpp/unittest/faiss_wrapper/wrapper_test.cpp
@@ -9,144 +9,130 @@
 #include "wrapper/Operand.h"
 #include "wrapper/Index.h"
 #include "wrapper/IndexBuilder.h"
-#include <regex>
+
 using namespace zilliz::milvus::engine;
 
-TEST(xxx, Wrapper_Test){
-    // std::string xxx = "dialect+driver://username:password@host:port/database";
 
-    //mysql://scott:tiger@localhost/mydatabase
+TEST(operand_test, Wrapper_Test) {
+    using std::cout;
+    using std::endl;
 
-    std::string littel_xx = "dixx://";
-    std::regex xx_regex("([a-zA-Z0-9_-\\.]*):\\/\\/([a-zA-Z0-9_-\\.]*)\\:([a-zA-Z0-9_-\\.]*)\\@([a-zA-Z0-9_-\\.]*)\\:([0-9]*)\\/([a-zA-Z0-9_-\\.]*)");
-    std::smatch pieces_match;
-    std::regex_match(littel_xx, pieces_match, xx_regex);
+    auto opd = std::make_shared<Operand>();
+    opd->index_type = "IVF";
+    opd->preproc = "OPQ";
+    opd->postproc = "PQ";
+    opd->metric_type = "L2";
+    opd->d = 64;
 
-    for(auto &x : pieces_match) {
-        std::cout << "hehhe: " << x.str() << std::endl;
-    }
+    auto opd_str = operand_to_str(opd);
+    auto new_opd = str_to_operand(opd_str);
+
+    // TODO: fix all place where using opd to build index.
+    assert(new_opd->get_index_type(10000) == opd->get_index_type(10000));
 }
 
-//TEST(operand_test, Wrapper_Test) {
-//    using std::cout;
-//    using std::endl;
-//
-//    auto opd = std::make_shared<Operand>();
-//    opd->index_type = "IVF";
-//    opd->preproc = "OPQ";
-//    opd->postproc = "PQ";
-//    opd->metric_type = "L2";
-//    opd->d = 64;
-//
-//    auto opd_str = operand_to_str(opd);
-//    auto new_opd = str_to_operand(opd_str);
-//
-//    // TODO: fix all place where using opd to build index.
-//    assert(new_opd->get_index_type(10000) == opd->get_index_type(10000));
-//}
-//
-//TEST(build_test, Wrapper_Test) {
-//    // dimension of the vectors to index
-//    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 = "IVF";
-//    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;
-//
-//    //prepare raw data
-//    xb.resize(nb);
-//    ids.resize(nb);
-//    for (size_t i = 0; i < nb; i++) {
-//        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);
-//    }
-//
-//    delete[] result_dists;
-//    delete[] result_ids;
-//}
-//
-//TEST(gpu_build_test, Wrapper_Test) {
-//    using std::vector;
-//
-//    int d = 256;
-//    int nb = 3 * 1000 * 100;
-//    int nq = 100;
-//    vector<float> xb(d * nb);
-//    vector<float> xq(d * nq);
-//    vector<long> ids(nb);
-//
-//    std::random_device rd;
-//    std::mt19937 gen(rd());
-//    std::uniform_real_distribution<> dis_xt(-1.0, 1.0);
-//    for (auto &e : xb) { e = float(dis_xt(gen)); }
-//    for (auto &e : xq) { e = float(dis_xt(gen)); }
-//    for (int i = 0; i < nb; ++i) { ids[i] = i; }
-//
-//    auto opd = std::make_shared<Operand>();
-//    opd->index_type = "IVF";
-//    opd->d = d;
-//    opd->ncent = 256;
-//
-//    IndexBuilderPtr index_builder_1 = GetIndexBuilder(opd);
-//    auto index_1 = index_builder_1->build_all(nb, xb.data(), ids.data());
-//    assert(index_1->ntotal == nb);
-//    assert(index_1->dim == d);
-//
-//    // sanity check: search 5 first vectors of xb
-//    int k = 1;
-//    vector<long> I(5 * k);
-//    vector<float> D(5 * k);
-//    index_1->search(5, xb.data(), k, D.data(), I.data());
-//    for (int i = 0; i < 5; ++i) { assert(i == I[i]); }
-//}
+TEST(build_test, Wrapper_Test) {
+    // dimension of the vectors to index
+    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 = "IVF";
+    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;
+
+    //prepare raw data
+    xb.resize(nb);
+    ids.resize(nb);
+    for (size_t i = 0; i < nb; i++) {
+        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);
+    }
+
+    delete[] result_dists;
+    delete[] result_ids;
+}
+
+TEST(gpu_build_test, Wrapper_Test) {
+    using std::vector;
+
+    int d = 256;
+    int nb = 3 * 1000 * 100;
+    int nq = 100;
+    vector<float> xb(d * nb);
+    vector<float> xq(d * nq);
+    vector<long> ids(nb);
+
+    std::random_device rd;
+    std::mt19937 gen(rd());
+    std::uniform_real_distribution<> dis_xt(-1.0, 1.0);
+    for (auto &e : xb) { e = float(dis_xt(gen)); }
+    for (auto &e : xq) { e = float(dis_xt(gen)); }
+    for (int i = 0; i < nb; ++i) { ids[i] = i; }
+
+    auto opd = std::make_shared<Operand>();
+    opd->index_type = "IVF";
+    opd->d = d;
+    opd->ncent = 256;
+
+    IndexBuilderPtr index_builder_1 = GetIndexBuilder(opd);
+    auto index_1 = index_builder_1->build_all(nb, xb.data(), ids.data());
+    assert(index_1->ntotal == nb);
+    assert(index_1->dim == d);
+
+    // sanity check: search 5 first vectors of xb
+    int k = 1;
+    vector<long> I(5 * k);
+    vector<float> D(5 * k);
+    index_1->search(5, xb.data(), k, D.data(), I.data());
+    for (int i = 0; i < 5; ++i) { assert(i == I[i]); }
+}