gitclonehttps://github.com/intel/ScalableVectorSearchcdScalableVectorSearch/examples/cpp/sharedmkdirbuild&&cdbuildcmake..make-j./example_vamana_with_compression

constsize_tnum_threads=4;size_tpadding=32;size_tleanvec_dim=64;autothreadpool=svs::threads::as_threadpool(num_threads);autoloaded=svs::VectorDataLoader<float>(std::filesystem::path(SVS_DATA_DIR)/"data_f32.svs").load();autodata=svs::leanvec::LeanDataset<svs::leanvec::UsingLVQ<4>,svs::leanvec::UsingLVQ<8>,svs::Dynamic,svs::Dynamic>::reduce(loaded,std::nullopt,threadpool,padding,svs::lib::MaybeStatic<svs::Dynamic>(leanvec_dim));

autoparameters=svs::index::vamana::VamanaBuildParameters{};svs::Vamanaindex=svs::Vamana::build<float>(parameters,data,svs::distance::DistanceL2(),num_threads);

constsize_tsearch_window_size=50;constsize_tn_neighbors=10;index.set_search_window_size(search_window_size);autoqueries=svs::load_data<float>(std::filesystem::path(SVS_DATA_DIR)/"queries_f32.fvecs");autoresults=index.search(queries,n_neighbors);

autogroundtruth=svs::load_data<int>(std::filesystem::path(SVS_DATA_DIR)/"groundtruth_euclidean.ivecs");doublerecall=svs::k_recall_at_n(groundtruth,results,n_neighbors,n_neighbors);fmt::print("Recall@{} = {:.4f}\n",n_neighbors,recall);

index.save("config","graph","data");index=svs::Vamana::assemble<float>("config",svs::GraphLoader("graph"),svs::lib::load_from_disk<svs::leanvec::LeanDataset<svs::leanvec::UsingLVQ<4>,svs::leanvec::UsingLVQ<8>,svs::Dynamic,svs::Dynamic>>("data",padding),svs::distance::DistanceL2(),num_threads);

/* * Copyright 2025 Intel Corporation * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * *     http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. */// SVS#include"svs/core/recall.h"#include"svs/extensions/flat/leanvec.h"#include"svs/extensions/flat/lvq.h"#include"svs/extensions/vamana/leanvec.h"#include"svs/extensions/vamana/lvq.h"#include"svs/orchestrators/dynamic_vamana.h"#include"svs/orchestrators/exhaustive.h"#include"svs/orchestrators/vamana.h"intmain(){// STEP 1: Compress Data with LeanVec, reducing dimensionality to leanvec_dim dimensions// and using 4 and 8 bits for primary and secondary levels respectively.//! [Compress data]constsize_tnum_threads=4;size_tpadding=32;size_tleanvec_dim=64;autothreadpool=svs::threads::as_threadpool(num_threads);autoloaded=svs::VectorDataLoader<float>(std::filesystem::path(SVS_DATA_DIR)/"data_f32.svs").load();autodata=svs::leanvec::LeanDataset<svs::leanvec::UsingLVQ<4>,svs::leanvec::UsingLVQ<8>,svs::Dynamic,svs::Dynamic>::reduce(loaded,std::nullopt,threadpool,padding,svs::lib::MaybeStatic<svs::Dynamic>(leanvec_dim));//! [Compress data]// STEP 2: Build Vamana Index//! [Index Build]autoparameters=svs::index::vamana::VamanaBuildParameters{};svs::Vamanaindex=svs::Vamana::build<float>(parameters,data,svs::distance::DistanceL2(),num_threads);//! [Index Build]// STEP 3: Search the Index//! [Perform Queries]constsize_tsearch_window_size=50;constsize_tn_neighbors=10;index.set_search_window_size(search_window_size);autoqueries=svs::load_data<float>(std::filesystem::path(SVS_DATA_DIR)/"queries_f32.fvecs");autoresults=index.search(queries,n_neighbors);//! [Perform Queries]//! [Recall]autogroundtruth=svs::load_data<int>(std::filesystem::path(SVS_DATA_DIR)/"groundtruth_euclidean.ivecs");doublerecall=svs::k_recall_at_n(groundtruth,results,n_neighbors,n_neighbors);fmt::print("Recall@{} = {:.4f}\n",n_neighbors,recall);//! [Recall]// STEP 4: Saving and reloading the index//! [Saving Loading]index.save("config","graph","data");index=svs::Vamana::assemble<float>("config",svs::GraphLoader("graph"),svs::lib::load_from_disk<svs::leanvec::LeanDataset<svs::leanvec::UsingLVQ<4>,svs::leanvec::UsingLVQ<8>,svs::Dynamic,svs::Dynamic>>("data",padding),svs::distance::DistanceL2(),num_threads);//! [Saving Loading]index.set_search_window_size(search_window_size);recall=svs::k_recall_at_n(groundtruth,results,n_neighbors,n_neighbors);fmt::print("Recall@{} after saving and reloading = {:.4f}\n",n_neighbors,recall);return0;}

gitclonehttps://github.com/intel/ScalableVectorSearchcdScalableVectorSearchmkdirbuild&&cdbuildcmake..-DSVS_BUILD_EXAMPLES=YEScmake--build.-j$(nproc)

examples/cpp/types

examples/cpp/vamana../data/test_dataset/data_f32.fvecs../data/test_dataset/queries_f32.fvecs../data/test_dataset/groundtruth_euclidean.ivecs

/* * Copyright 2023 Intel Corporation * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * *     http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *///! [Example All]//! [Includes]// SVS Dependencies#include"svs/orchestrators/vamana.h"       // bulk of the dependencies required.#include"svs/core/recall.h"                // Convenient k-recall@n computation.#include"svs/extensions/vamana/scalar.h"   // SQ vamana extensions.#include"svs/quantization/scalar/scalar.h" // SQ implementation.// Alternative main definition#include"svsmain.h"// stl#include<map>#include<string>#include<string_view>#include<vector>//! [Includes]//! [Helper Utilities]doublerun_recall(svs::Vamana&index,constsvs::data::SimpleData<float>&queries,constsvs::data::SimpleData<uint32_t>&groundtruth,size_tsearch_window_size,size_tnum_neighbors,std::string_viewmessage=""){index.set_search_window_size(search_window_size);autoresults=index.search(queries,num_neighbors);doublerecall=svs::k_recall_at_n(groundtruth,results,num_neighbors,num_neighbors);if(!message.empty()){fmt::print("[{}] ",message);}fmt::print("Windowsize = {}, Recall = {}\n",search_window_size,recall);returnrecall;}constboolDEBUG=false;voidcheck(doubleexpected,doublegot,doubleeps=0.005){doublediff=std::abs(expected-got);ifconstexpr(DEBUG){fmt::print("Expected {}. Got {}\n",expected,got);}else{if(diff>eps){throwANNEXCEPTION("Expected ",expected,". Got ",got,'!');}}}//! [Helper Utilities]// Alternative main definitionintsvs_main(std::vector<std::string>args){//! [Argument Extraction]constsize_tnargs=args.size();if(nargs!=4){throwANNEXCEPTION("Expected 3 arguments. Instead, got ",nargs,'!');}conststd::string&data_vecs=args.at(1);conststd::string&query_vecs=args.at(2);conststd::string&groundtruth_vecs=args.at(3);//! [Argument Extraction]// Building the index//! [Build Parameters]autoparameters=svs::index::vamana::VamanaBuildParameters{1.2,// alpha64,// graph max degree128,// search window size1024,// max candidate pool size60,// prune to degreetrue,// full search history};//! [Build Parameters]//! [Index Build]size_tnum_threads=4;svs::Vamanaindex=svs::Vamana::build<float>(parameters,svs::VectorDataLoader<float>(data_vecs),svs::DistanceL2(),num_threads);//! [Index Build]// Searching the index//! [Load Aux]// Load the queries and ground truth.autoqueries=svs::load_data<float>(query_vecs);autogroundtruth=svs::load_data<uint32_t>(groundtruth_vecs);//! [Load Aux]//! [Perform Queries]index.set_search_window_size(30);svs::QueryResult<size_t>results=index.search(queries,10);doublerecall=svs::k_recall_at_n(groundtruth,results);check(0.8215,recall);//! [Perform Queries]//! [Search Window Size]autoexpected_recall=std::map<size_t,double>({{10,0.5509},{20,0.7281},{30,0.8215},{40,0.8788}});for(autowindowsize:{10,20,30,40}){recall=run_recall(index,queries,groundtruth,windowsize,10,"Sweep");check(expected_recall.at(windowsize),recall);}//! [Search Window Size]// Saving the index//! [Saving]index.save("example_config","example_graph","example_data");//! [Saving]// Reloading a saved index//! [Loading]// We can reload an index from a previously saved set of files.index=svs::Vamana::assemble<float>("example_config",svs::GraphLoader("example_graph"),svs::VectorDataLoader<float>("example_data"),svs::DistanceType::L2,4// num_threads);recall=run_recall(index,queries,groundtruth,30,10,"Reload");check(0.8215,recall);//! [Loading]//! [Only Loading]// We can reload an index from a previously saved set of files.index=svs::Vamana::assemble<float>("example_config",svs::GraphLoader("example_graph"),svs::VectorDataLoader<float>("example_data"),svs::DistanceType::L2,4// num_threads);//! [Only Loading]//! [Set a new thread pool with n-threads]index.set_threadpool(svs::threads::DefaultThreadPool(4));//! [Set a new thread pool with n-threads]//! [Compressed Loader]// Quantizationnamespacescalar=svs::quantization::scalar;// Wrap the compressor object in a lazy functor.// This will defer loading and compression of the SQ dataset until the threadpool// used in the index has been created.autocompressor=svs::lib::Lazy([=](svs::threads::ThreadPoolauto&threadpool){autodata=svs::VectorDataLoader<float,128>("example_data").load();returnscalar::SQDataset<std::int8_t,128>::compress(data,threadpool);});index=svs::Vamana::assemble<float>("example_config",svs::GraphLoader("example_graph"),compressor,svs::DistanceType::L2,4);recall=run_recall(index,queries,groundtruth,30,10,"Compressed load");check(0.8190,recall);//! [Compressed Loader]//! [Build Index Compressed]// Compressed buildingindex=svs::Vamana::build<float>(parameters,compressor,svs::DistanceL2(),num_threads);recall=run_recall(index,queries,groundtruth,30,10,"Compressed Build");check(0.8212,recall);//! [Build Index Compressed]return0;}// Special main providing some helpful utilities.SVS_DEFINE_MAIN();//! [Example All]