Types

enum class Location {
    kRedis,      // In-memory storage via Redis
    kMemory,     // Temporary in-memory storage
    kPostgres,   // Relational database storage
    kNone        // Undefined storage type
};

DBConfig(Location location,
                const std::optional<std::string>& table_name,
                const std::optional<std::string>& db_connection_string);

cyborg::DBConfig index_loc(Location::kRedis, std::nullopt, "redis://localhost");
cyborg::DBConfig config_loc(Location::kRedis, std::nullopt, "redis://localhost");
cyborg::DBConfig items_loc(Location::kPostgres, "items", "host=localhost dbname=postgres");

DeviceConfig(const int cpu_threads = 0, const bool gpu_accelerate = false);

cyborg::DeviceConfig device_config(4, true);  // 4 CPU threads, GPU enabled
int threads = device_config.cpu_threads();    // Returns 4
bool use_gpu = device_config.gpu_accelerate(); // Returns true

enum class DistanceMetric {
    Cosine,
    Euclidean,
    SquaredEuclidean};

IndexIVF(size_t dimension = 0, std::optional<std::string> embedding_model = "");

IndexIVFFlat(size_t dimension = 0, std::optional<std::string> embedding_model = "");

IndexIVFPQ(size_t dimension = 0, size_t pq_dim = 16, size_t pq_bits = 8, 
           std::optional<std::string> embedding_model = "");

Array2D(size_t rows, size_t cols, const T& initial_value = T());
Array2D(std::vector<T>&& data, size_t cols);
Array2D(const std::vector<T>& data, size_t cols);

// Converting a vector to an array
std::vector<uint8_t> vec = {0, 1, 2, 3, 4, 5, 6, 7};
cyborg::Array2D<uint8_t> arr(vec, 2);
// arr is now a 2D array of 4 rows and 2 columns, with the contents from vec

// Creating a 2D array with 3 rows and 2 columns, initialized to zero
cyborg::Array2D<int> array(3, 2, 0);

// Access and modify elements
array(0, 0) = 1;
array(0, 1) = 2;

// Printing the array
for (size_t i = 0; i < array.rows(); ++i) {
    for (size_t j = 0; j < array.cols(); ++j) {
        std::cout << array(i, j) << " ";
    }
    std::cout << std::endl;
}

TrainingConfig(size_t n_lists = 0,
               size_t batch_size = 0,
               size_t max_iters = 0,
               double tolerance = 1e-6,
               size_t max_memory = 0);

QueryParams(size_t top_k = 100,
            size_t n_probes = 0,
            std::string filters = "",
            std::vector<ResultFields> include = {},
            bool greedy = false);

QueryResults results(num_queries, top_k);

// Access the top-k results for each query
for (size_t i = 0; i < num_queries; ++i) {
    auto result = results[i];
    for (size_t j = 0; j < result.num_results; ++j) {
        std::cout << "ID: " << result.ids[j] << ", Distance: " << result.distances[j] << std::endl;
    }
}

// Get the IDs and distances for all queries
auto all_ids = results.ids();
auto all_distances = results.distances();

using ItemID = std::string;

enum IndexType {
#ifdef LITE
    IVFFLAT  // Only IVFFLAT available in Lite mode
#else
    IVF,     // Inverted File index
    IVFPQ,   // Inverted File with Product Quantization
    IVFFLAT  // Inverted File with flat (uncompressed) storage
#endif
};

struct Item {
    const std::string id;                   // Item ID
    const std::vector<float> vector;        // Vector embedding
    const std::vector<uint8_t> contents;    // Decrypted contents
    const std::string metadata;             // Metadata (JSON string)
};

enum class ResultFields {
    kDistance,    // Include distance scores in query results
    kMetadata     // Include metadata in query results
};

enum class ItemFields {
    kVector,       // Include vector in returned items
    kMetadata,     // Include metadata in returned items
    kContents      // Include content data in returned items
};