diff --git a/deploy/vector_search/Makefile b/deploy/vector_search/Makefile
new file mode 100644
index 000000000..6a4ce215b
--- /dev/null
+++ b/deploy/vector_search/Makefile
@@ -0,0 +1,6 @@
+CXX=/usr/bin/g++-5
+
+all : index
+
+index.so : src/config.h src/graph.h src/data.h interface.cc
+	$(CXX) -shared -fPIC interface.cc -o index.so -std=c++11 -Ofast  -march=native -g -flto -funroll-loops -DOMP -fopenmp
diff --git a/deploy/vector_search/index.so b/deploy/vector_search/index.so
new file mode 100644
index 000000000..3ad2e3c8c
Binary files /dev/null and b/deploy/vector_search/index.so differ
diff --git a/deploy/vector_search/interface.cc b/deploy/vector_search/interface.cc
new file mode 100644
index 000000000..8c2640b5b
--- /dev/null
+++ b/deploy/vector_search/interface.cc
@@ -0,0 +1,242 @@
+#include<stdio.h>
+#include<string.h>
+#include <iostream>
+#include <fstream>
+#include <queue>
+#include <chrono>
+#include <unordered_set>
+#include <unordered_map>
+#include<stdlib.h>
+#include<memory>
+#include<vector>
+#include<functional>
+
+#include"src/data.h"
+#include"src/graph.h"
+
+struct IndexContext{
+    void* graph;
+    void* data;
+};
+
+
+int topk = 0;
+int display_topk = 1;
+int build_idx_offset = 0;
+int query_idx_offset = 0;
+
+void flush_add_buffer(
+        std::vector<std::pair<idx_t,std::vector<std::pair<int,value_t>>>>& add_buffer,
+        GraphWrapper* graph){
+    #pragma omp parallel for
+    for(int i = 0;i < add_buffer.size();++i){
+        auto& idx = add_buffer[i].first;
+        auto& point = add_buffer[i].second;
+        graph->add_vertex_lock(idx,point);
+    }
+    add_buffer.clear();
+}
+
+
+extern "C"{
+// for mobius IP index
+void build_mobius_index(float* dense_mat,int row,int dim, int pq_size, double mobius_pow , const char* prefix){
+    std::unique_ptr<Data> data;
+    std::unique_ptr<Data> data_original;
+    std::unique_ptr<GraphWrapper> graph; 
+    int topk = 0;
+    int display_topk = 1;
+    int build_idx_offset = 0;
+    int query_idx_offset = 0;
+        
+    ++row;
+    data = std::unique_ptr<Data>(new Data(row,dim));
+    graph = std::unique_ptr<GraphWrapper>(new FixedDegreeGraph<3>(data.get())); 
+    graph->set_construct_pq_size(pq_size);   
+ 
+    std::vector<std::pair<idx_t,std::vector<std::pair<int,value_t>>>> add_buffer;
+    
+    ((FixedDegreeGraph<3>*)graph.get())->get_data()->mobius_pow = mobius_pow;
+    data_original = std::unique_ptr<Data>(new Data(row,dim));
+
+    std::vector<std::pair<int,value_t>> dummy_mobius_point;
+    for(int i = 0;i < dim;++i)
+        dummy_mobius_point.push_back(std::make_pair(i,0));
+    
+    //idx += build_idx_offset;
+    
+    for(int i = 0;i < row - 1;++i){
+
+        std::vector<std::pair<int,value_t>> point;
+        point.reserve(dim);
+        for(int j = 0;j < dim;++j)
+            point.push_back(std::make_pair(j,dense_mat[i * dim + j]));
+
+        data_original->add(i,point);
+        data->add_mobius(i,point);
+        if(i < 1000){
+            graph->add_vertex(i,point);
+        }else{
+            add_buffer.push_back(std::make_pair(i,point));
+        }
+        if(add_buffer.size() >= 1000000)
+            flush_add_buffer(add_buffer,graph.get());
+    }
+    flush_add_buffer(add_buffer,graph.get());
+    graph->add_vertex(row - 1,dummy_mobius_point);
+    data.swap(data_original);
+
+    std::string str = std::string(prefix);
+    data->dump(str + ".data");
+    graph->dump(str + ".graph");
+ 
+}
+
+void load_mobius_index_prefix(int row,int dim,IndexContext* index_context,const char* prefix){
+    std::string str = std::string(prefix);
+        
+    ++row;
+    Data* data = new Data(row,dim);
+    GraphWrapper* graph = new FixedDegreeGraph<1>(data); 
+    
+    //idx += build_idx_offset;
+    data->load(str + ".data");
+    graph->load(str + ".graph");
+
+    ((FixedDegreeGraph<1>*)graph)->search_start_point = row - 1;
+    ((FixedDegreeGraph<1>*)graph)->ignore_startpoint = true;
+
+    index_context->graph = graph;
+    index_context->data = data;
+}
+
+void save_mobius_index_prefix(IndexContext* index_context,const char* prefix){
+    std::string str = std::string(prefix);
+    Data* data = (Data*)(index_context->data);
+    GraphWrapper* graph = (GraphWrapper*)(index_context->graph); 
+
+    data->dump(str + ".data");
+    graph->dump(str + ".graph");
+}
+
+void search_mobius_index(float* dense_vec,int dim,int search_budget,int return_k, IndexContext* index_context,idx_t* ret_id,double* ret_score){
+    int topk = 0;
+    int display_topk = 1;
+    int build_idx_offset = 0;
+    int query_idx_offset = 0;
+      
+    Data* data = reinterpret_cast<Data*>(index_context->data);
+    GraphWrapper* graph = reinterpret_cast<GraphWrapper*>(index_context->graph);
+   
+ 
+    //auto flag = (data==NULL);
+    //std::cout<<flag<<std::endl;
+
+    std::vector<std::pair<int,value_t>> point;
+    point.reserve(dim);
+    for(int j = 0;j < dim;++j)
+        point.push_back(std::make_pair(j,dense_vec[j]));
+    std::vector<idx_t> topN;
+    std::vector<double> score;
+    graph->search_top_k_with_score(point,search_budget,topN,score);
+    for(int i = 0;i < topN.size() && i < return_k;++i){
+        ret_id[i] = topN[i];
+        ret_score[i] = score[i];
+    }
+}
+
+
+// For L2 index
+void build_l2_index(float* dense_mat,int row,int dim, int pq_size, const char* prefix){
+    std::unique_ptr<Data> data;
+    std::unique_ptr<GraphWrapper> graph; 
+    int topk = 0;
+    int display_topk = 1;
+    int build_idx_offset = 0;
+    int query_idx_offset = 0;
+        
+    data = std::unique_ptr<Data>(new Data(row,dim));
+    graph = std::unique_ptr<GraphWrapper>(new FixedDegreeGraph<3>(data.get())); 
+    graph->set_construct_pq_size(pq_size);
+
+    std::vector<std::pair<idx_t,std::vector<std::pair<int,value_t>>>> add_buffer;
+    
+    for(int i = 0;i < row;++i){
+        std::vector<std::pair<int,value_t>> point;
+        point.reserve(dim);
+        for(int j = 0;j < dim;++j)
+            point.push_back(std::make_pair(j,dense_mat[i * dim + j]));
+        data->add(i,point);
+        if(i < 1000){
+            graph->add_vertex(i,point);
+        }else{
+            add_buffer.push_back(std::make_pair(i,point));
+        }
+        if(add_buffer.size() >= 1000000)
+            flush_add_buffer(add_buffer,graph.get());
+    }
+    flush_add_buffer(add_buffer,graph.get());
+    
+    std::string str = std::string(prefix);
+    data->dump(str + ".data");
+    graph->dump(str + ".graph");
+
+}
+
+void load_l2_index_prefix(int row,int dim,IndexContext* index_context,const char* prefix){
+    std::string str = std::string(prefix);
+        
+    Data* data = new Data(row,dim);
+    GraphWrapper* graph = new FixedDegreeGraph<3>(data); 
+    
+    //idx += build_idx_offset;
+
+    data->load(str + ".data");
+    graph->load(str + ".graph");
+
+    index_context->graph = graph;
+    index_context->data = data;
+}
+
+void save_l2_index_prefix(IndexContext* index_context,const char* prefix){
+    std::string str = std::string(prefix);
+    Data* data = (Data*)(index_context->data);
+    GraphWrapper* graph = (GraphWrapper*)(index_context->graph); 
+
+    data->dump(str + ".data");
+    graph->dump(str + ".graph");
+}
+
+
+
+void search_l2_index(float* dense_vec,int dim,int search_budget,int return_k, IndexContext* index_context,idx_t* ret_id,double* ret_score){
+    int topk = 0;
+    int display_topk = 1;
+    int build_idx_offset = 0;
+    int query_idx_offset = 0;
+        
+    Data* data = reinterpret_cast<Data*>(index_context->data);
+    GraphWrapper* graph = reinterpret_cast<GraphWrapper*>(index_context->graph);
+
+    std::vector<std::pair<int,value_t>> point;
+    point.reserve(dim);
+    for(int j = 0;j < dim;++j)
+        point.push_back(std::make_pair(j,dense_vec[j]));
+    std::vector<idx_t> topN;
+    std::vector<double> score;
+    graph->search_top_k_with_score(point,search_budget,topN,score);
+    for(int i = 0;i < topN.size() && i < return_k;++i){
+//        printf("%d: (%zu, %f)\n",i,topN[i],score[i]);
+        ret_id[i] = topN[i];
+        ret_score[i] = score[i];
+    }
+}
+
+
+void release_context(IndexContext* index_context){
+    delete (Data*)(index_context->data);
+    delete (GraphWrapper*)(index_context->graph);
+}
+
+} // extern "C"
+
diff --git a/deploy/vector_search/interface.py b/deploy/vector_search/interface.py
new file mode 100644
index 000000000..e438d233d
--- /dev/null
+++ b/deploy/vector_search/interface.py
@@ -0,0 +1,166 @@
+import ctypes
+import paddle
+import numpy.ctypeslib as ctl
+import numpy as np
+import os
+import json
+
+from ctypes import *
+from numpy.ctypeslib import ndpointer
+
+lib = ctypes.cdll.LoadLibrary("./index.so")
+
+class IndexContext(Structure):
+    _fields_=[("graph",c_void_p),
+              ("data",c_void_p)]
+
+# for mobius IP index
+build_mobius_index = lib.build_mobius_index
+build_mobius_index.restype = None
+build_mobius_index.argtypes = [ctl.ndpointer(np.float32, flags='aligned, c_contiguous'), ctypes.c_int, ctypes.c_int, ctypes.c_int, ctypes.c_double, ctypes.c_char_p]
+
+search_mobius_index = lib.search_mobius_index
+search_mobius_index.restype = None
+search_mobius_index.argtypes = [ctl.ndpointer(np.float32, flags='aligned, c_contiguous'), ctypes.c_int, ctypes.c_int,ctypes.c_int,POINTER(IndexContext),ctl.ndpointer(np.uint64, flags='aligned, c_contiguous'),ctl.ndpointer(np.float64, flags='aligned, c_contiguous')]
+
+load_mobius_index_prefix = lib.load_mobius_index_prefix
+load_mobius_index_prefix.restype = None
+load_mobius_index_prefix.argtypes = [ctypes.c_int, ctypes.c_int, POINTER(IndexContext), ctypes.c_char_p]
+
+save_mobius_index_prefix = lib.save_mobius_index_prefix
+save_mobius_index_prefix.restype = None
+save_mobius_index_prefix.argtypes = [POINTER(IndexContext), ctypes.c_char_p]
+
+
+# for L2 index
+build_l2_index = lib.build_l2_index
+build_l2_index.restype = None
+build_l2_index.argtypes = [ctl.ndpointer(np.float32, flags='aligned, c_contiguous'), ctypes.c_int, ctypes.c_int, ctypes.c_int, ctypes.c_char_p]
+
+search_l2_index = lib.search_l2_index
+search_l2_index.restype = None
+search_l2_index.argtypes = [ctl.ndpointer(np.float32, flags='aligned, c_contiguous'), ctypes.c_int, ctypes.c_int,ctypes.c_int,POINTER(IndexContext),ctl.ndpointer(np.uint64, flags='aligned, c_contiguous'),ctl.ndpointer(np.float64, flags='aligned, c_contiguous')]
+
+load_l2_index_prefix = lib.load_l2_index_prefix
+load_l2_index_prefix.restype = None
+load_l2_index_prefix.argtypes = [ctypes.c_int, ctypes.c_int, POINTER(IndexContext), ctypes.c_char_p]
+
+save_l2_index_prefix = lib.save_l2_index_prefix
+save_l2_index_prefix.restype = None
+save_l2_index_prefix.argtypes = [POINTER(IndexContext), ctypes.c_char_p]
+
+release_context = lib.release_context
+release_context.restype = None
+release_context.argtypes = [POINTER(IndexContext)]
+
+
+
+class Graph_Index(object):
+    """
+        graph index
+    """
+    def __init__(self, dist_type="IP"):
+        self.dim = 0
+        self.total_num = 0
+        self.dist_type = dist_type
+        self.mobius_pow = 2.0
+        self.index_context = IndexContext(0,0)
+        self.gallery_doc_dict = {}
+        self.with_attr = False
+        assert dist_type in ["IP", "L2"], "Only support IP and L2 distance ..."
+    
+    def build(self, gallery_vectors, gallery_docs=[], pq_size=100, index_path='graph_index/'):
+        """
+        build index 
+        """
+        if paddle.is_tensor(gallery_vectors):
+              gallery_vectors = gallery_vectors.numpy()
+        assert gallery_vectors.ndim == 2, "Input vector must be 2D ..."
+        
+        self.total_num = gallery_vectors.shape[0]
+        self.dim = gallery_vectors.shape[1]
+
+        assert (len(gallery_docs) == self.total_num if len(gallery_docs)>0 else True)
+ 
+        print("training index -> num: {}, dim: {}, dist_type: {}".format(self.total_num, self.dim, self.dist_type))
+
+        if not os.path.exists(index_path):
+            os.makedirs(index_path)
+ 
+        if self.dist_type == "IP":
+            build_mobius_index(gallery_vectors,self.total_num,self.dim, pq_size, self.mobius_pow, create_string_buffer((index_path+"/index").encode('utf-8')))
+            load_mobius_index_prefix(self.total_num, self.dim, ctypes.byref(self.index_context), create_string_buffer((index_path+"/index").encode('utf-8')))
+        else:
+            build_l2_index(gallery_vectors,self.total_num,self.dim, pq_size, create_string_buffer((index_path+"/index").encode('utf-8')))
+            load_l2_index_prefix(self.total_num, self.dim, ctypes.byref(self.index_context), create_string_buffer((index_path+"/index").encode('utf-8')))
+        
+        self.gallery_doc_dict = {}       
+        if len(gallery_docs) > 0:
+            self.with_attr = True
+            for i in range(gallery_vectors.shape[0]):
+                self.gallery_doc_dict[str(i)] = gallery_docs[i] 
+
+        self.gallery_doc_dict["total_num"] = self.total_num
+        self.gallery_doc_dict["dim"] = self.dim
+        self.gallery_doc_dict["dist_type"] = self.dist_type
+        self.gallery_doc_dict["with_attr"] = self.with_attr
+
+        with open(index_path + "/info.json", "w") as f:
+            json.dump(self.gallery_doc_dict, f)
+
+        print("finished creating index ...")
+
+    def search(self, query, return_k=10, search_budget=100):
+        """
+        search
+        """
+        ret_id = np.zeros(return_k, dtype=np.uint64)
+        ret_score = np.zeros(return_k, dtype=np.float64)
+
+        if paddle.is_tensor(query):
+              query = query.numpy()
+        if self.dist_type == "IP":
+            search_mobius_index(query,self.dim,search_budget,return_k,ctypes.byref(self.index_context),ret_id,ret_score)
+        else:
+            search_l2_index(query,self.dim,search_budget,return_k,ctypes.byref(self.index_context),ret_id,ret_score)
+         
+        ret_id = ret_id.tolist()
+        ret_doc = []
+        if self.with_attr: 
+            for i in range(return_k):
+                ret_doc.append(self.gallery_doc_dict[str(ret_id[i])])
+            return ret_score, ret_doc
+        else:
+            return ret_score, ret_id
+
+    def dump(self, index_path):
+
+        if not os.path.exists(index_path):
+            os.makedirs(index_path)
+
+        if self.dist_type == "IP":
+            save_mobius_index_prefix(ctypes.byref(self.index_context),create_string_buffer((index_path+"/index").encode('utf-8')))
+        else:
+            save_l2_index_prefix(ctypes.byref(self.index_context), create_string_buffer((index_path+"/index").encode('utf-8')))
+        
+        with open(index_path + "/info.json", "w") as f:
+            json.dump(self.gallery_doc_dict, f)
+
+    def load(self, index_path):
+        self.gallery_doc_dict = {}
+        
+        with open(index_path + "/info.json", "r") as f:
+            self.gallery_doc_dict = json.load(f)
+        
+        self.total_num = self.gallery_doc_dict["total_num"]
+        self.dim = self.gallery_doc_dict["dim"]
+        self.dist_type = self.gallery_doc_dict["dist_type"]    
+        self.with_attr = self.gallery_doc_dict["with_attr"]
+
+        if self.dist_type == "IP":
+            load_mobius_index_prefix(self.total_num,self.dim,ctypes.byref(self.index_context), create_string_buffer((index_path+"/index").encode('utf-8')))
+        else:
+            load_l2_index_prefix(self.total_num,self.dim,ctypes.byref(self.index_context), create_string_buffer((index_path+"/index").encode('utf-8')))
+
+
+
diff --git a/deploy/vector_search/test.py b/deploy/vector_search/test.py
new file mode 100644
index 000000000..60bce6d7b
--- /dev/null
+++ b/deploy/vector_search/test.py
@@ -0,0 +1,20 @@
+import numpy as np
+from interface import *
+
+# 随机产生样本
+index_vectors = np.random.rand(100000,128).astype(np.float32) 
+query_vector = np.random.rand(128).astype(np.float32) 
+index_docs = ["ID_"+str(i) for i in range(100000)]
+
+# 初始化索引结构
+indexer = Graph_Index(dist_type="IP") #支持"IP"和"L2"
+indexer.build(gallery_vectors=index_vectors, gallery_docs=index_docs, pq_size=100, index_path='test')
+
+# 查询
+scores, docs = indexer.search(query=query_vector, return_k=10, search_budget=100)
+print(scores)
+print(docs)
+
+# 保存与加载
+indexer.dump(index_path="test") 
+indexer.load(index_path="test")