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129 lines
4.1 KiB
C++
129 lines
4.1 KiB
C++
#include <gtest/gtest.h>
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#include <iostream>
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#include <random>
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#include <string>
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#include <thread>
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#include <vector>
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#include "dog_segment/ConcurrentVector.h"
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#include "dog_segment/SegmentBase.h"
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// #include "knowhere/index/vector_index/helpers/IndexParameter.h"
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#include "dog_segment/SegmentBase.h"
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#include "dog_segment/AckResponder.h"
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#include <knowhere/index/vector_index/VecIndex.h>
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#include <knowhere/index/vector_index/adapter/VectorAdapter.h>
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#include <knowhere/index/vector_index/VecIndexFactory.h>
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using std::cin;
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using std::cout;
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using std::endl;
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using namespace milvus::engine;
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using namespace milvus::dog_segment;
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using std::vector;
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using namespace milvus;
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namespace {
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template<int DIM>
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auto generate_data(int N) {
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std::vector<char> raw_data;
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std::vector<uint64_t> timestamps;
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std::vector<int64_t> uids;
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std::default_random_engine er(42);
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std::uniform_real_distribution<> distribution(0.0, 1.0);
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std::default_random_engine ei(42);
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for (int i = 0; i < N; ++i) {
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uids.push_back(10 * N + i);
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timestamps.push_back(0);
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// append vec
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float vec[DIM];
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for (auto &x: vec) {
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x = distribution(er);
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}
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raw_data.insert(raw_data.end(), (const char *) std::begin(vec), (const char *) std::end(vec));
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// int age = ei() % 100;
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// raw_data.insert(raw_data.end(), (const char *) &age, ((const char *) &age) + sizeof(age));
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}
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return std::make_tuple(raw_data, timestamps, uids);
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}
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}
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TEST(TestIndex, Naive) {
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constexpr int N = 100000;
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constexpr int DIM = 16;
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constexpr int TOPK = 10;
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auto[raw_data, timestamps, uids] = generate_data<DIM>(N);
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auto index = knowhere::VecIndexFactory::GetInstance().CreateVecIndex(knowhere::IndexEnum::INDEX_FAISS_IVFPQ,
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knowhere::IndexMode::MODE_CPU);
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auto conf = milvus::knowhere::Config{
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{milvus::knowhere::meta::DIM, DIM},
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{milvus::knowhere::meta::TOPK, TOPK},
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{milvus::knowhere::IndexParams::nlist, 100},
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{milvus::knowhere::IndexParams::nprobe, 4},
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{milvus::knowhere::IndexParams::m, 4},
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{milvus::knowhere::IndexParams::nbits, 8},
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{milvus::knowhere::Metric::TYPE, milvus::knowhere::Metric::L2},
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{milvus::knowhere::meta::DEVICEID, 0},
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};
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// auto ds = knowhere::GenDataset(N, DIM, raw_data.data());
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// auto ds2 = knowhere::GenDatasetWithIds(N / 2, DIM, raw_data.data() + sizeof(float[DIM]) * N / 2, uids.data() + N / 2);
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// NOTE: you must train first and then add
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// index->Train(ds, conf);
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// index->Train(ds2, conf);
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// index->AddWithoutIds(ds, conf);
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// index->Add(ds2, conf);
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std::vector<knowhere::DatasetPtr> datasets;
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std::vector<std::vector<float>> ftrashs;
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for (int beg = 0; beg < N; beg += N) {
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auto end = beg + N;
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if (end > N) {
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end = N;
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}
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std::vector<float> ft(raw_data.data() + DIM * beg, raw_data.data() + DIM * end);
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auto ds = knowhere::GenDataset(end - beg, DIM, ft.data());
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datasets.push_back(ds);
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ftrashs.push_back(std::move(ft));
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// // NOTE: you must train first and then add
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// index->Train(ds, conf);
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// index->Add(ds, conf);
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}
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for (auto &ds: datasets) {
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index->Train(ds, conf);
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}
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for (auto &ds: datasets) {
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index->AddWithoutIds(ds, conf);
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}
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auto bitmap = std::make_shared<faiss::ConcurrentBitset>(N);
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// exclude the first
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for (int i = 0; i < N / 2; ++i) {
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bitmap->set(i);
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}
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index->SetBlacklist(bitmap);
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auto query_ds = knowhere::GenDataset(1, DIM, raw_data.data());
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auto final = index->Query(query_ds, conf);
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auto ids = final->Get<idx_t *>(knowhere::meta::IDS);
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auto distances = final->Get<float *>(knowhere::meta::DISTANCE);
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for (int i = 0; i < TOPK; ++i) {
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if (ids[i] < N / 2) {
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cout << "WRONG: ";
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}
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cout << ids[i] << "->" << distances[i] << endl;
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}
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int i = 1 + 1;
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}
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