Examples

Assume that the directory where the program runs is /path/to/hnswlib. The complete directory structure is as follows:

├── configs          // Configuration files related to datasets
├── hnswlib           // hnswlib header files
├── python_bindings    // Python API files
├── include           // Performance test files
├── src              // Performance test header files
├── examples         // Function test files
├── tests             // Function test files
├── test               // Test files, including UT and fuzz test files
└── ALGO_PARAMS.md
└── CMakeLists.txt     // CMakeLists.txt file for function tests
└── LICENSE
└── Makefile           // Makefile file for performance tests
└── MANIFEST.in
└── pyproject.toml
└── README.en.md
└── README.md
└── run.sh             // Script for compiling and executing function tests
└── test.sh           // Script for executing performance tests
└── setup.py
└── TESTING_RECALL.md

The following example demonstrates how to create an index, insert elements, perform search, save the index, and load the index.

#include "hnswlib/hnswlib.h"

int main() {
    int dim = 128;
    int M = 16;
    int efc = 200;
    int efs = 30;
    int k = 10;
    int num_points = 1000;
    int num_queries = 100;
    string distance_type = "l2";
    string data_type = "fp16";

    // Generate FP32 test data.
    std::default_random_engine engine(5678);
    std::uniform_real_distribution<float> distribution(0.0f, 1.0f);
    std::vector<float> data(num_points * dim);   
    for (auto& val : data) {
        val = distribution(engine);   
    }
    std::vector<float> query(num_queries * dim);
    for (auto& val : query) {
        val = distribution(engine);
    }
    // Convert the data type to FP16.
    vector<float16_t> data_fp16(num_points * dim);
    for (int i = 0; i < num_points * dim; i++) {
        data_fp16[i] = static_cast<float16_t>(data[i]);
    }
    vector<float16_t> query_fp16(num_queries * dim);
    for (int i = 0; i < num_queries * dim; i++) {
        query_fp16[i] = static_cast<float16_t>(query[i]);
    }

    // Create the distance computation space and HNSW graph.
    auto space = std::make_unique<L2SpaceHalf>(dim);
    auto index = std::make_unique<HierarchicalNSWHalf>(space.get(), num_points, M, efc, 100, false);

    // Create Index.
    for (int i = 0; i < num_points; i++) {
        index->addPoint(&data_fp16[i * dim], i);
    }
    if (num_points > 0) {
        char* hnsw_raw = index->data_level0_memory_;
        labeltype* hnsw_label_ptr = reinterpret_cast<labeltype*>(hnsw_raw + index->label_offset_);
    }

    // Perform search.
    index->setEf(efs);
    vector<vector<pair<float, labeltype>>> results;
    for (int i = 0; i < num_queries; i++) {
        auto result = index->searchKnn(&query[i * dim], k);
        vector<pair<float, labeltype>> result_vec;
        while (!result.empty()) {
            result_vec.push_back(result.top());
            result.pop();
        }
        std::reverse(result_vec.begin(), result_vec.end());
        results.push_back(result_vec);
    }

    // Save the index.
    string index_file = "test_index_" + distance_type + "_" + data_type + ".bin";
    try{
        index->saveIndex(index_file);
    } catch (const std::exception& e) {
        std::cerr << "Failed to save index: " << e.what() << std::endl;
        index = nullptr;
    }

    // Load the index.
    auto loaded_index = std::make_unique<HierarchicalNSWHalf>(space.get(), 0, M, efc, 100, false);
    loaded_index->loadIndex(index_file, space);
    loaded_index->setEf(efs);
}

├── configs
      ├── hnswlib
      ├── hnswlib_fp16
            └── hnswlib_fp16_fashion-mnist-784-euclidean.config

The content of the config file is as follows:

# HNSWLIB
k_f = 16
efs = 30
efc = 200
metric = L2
nloop = 3
num_threads = 64
top_k = 10
batch_mode = false
batch_size = 100
save_or_load = save
index_path = indexes/hnsw/fashion.faiss