Location
enum contains the supported index backing store locations for CyborgDB. These are:
DBConfig
defines the storage location for various index components.
Parameter | Type | Description |
---|---|---|
location | Location | Specifies the type of storage location. |
table_name | std::string | (Optional) Name of the table in the database, if applicable. |
db_connection_string | std::string | (Optional) Connection string for database access, if applicable. |
DeviceConfig
class holds the configuration details for the device used in vector search operations, such as the number of CPU threads and whether to accelerate computations using a GPU.
Parameter | Type | Description |
---|---|---|
cpu_threads | int | (Optional) Number of CPU threads to use. Defaults to 0 (use all available cores). |
gpu_accelerate | bool | (Optional) Whether to use GPU acceleration. Defaults to false . |
Method | Return Type | Description |
---|---|---|
cpu_threads() const | int | Get the number of CPU threads configured. |
gpu_accelerate() const | bool | Check if GPU acceleration is enabled. |
DistanceMetric
enum contains the supported distance metrics for CyborgDB. These are:
IndexConfig
is an abstract base class for configuring index types. The three derived classes can be used to configure indexes:
Speed | Recall | Index Size |
---|---|---|
Fastest | Lowest | Smallest |
Parameter | Type | Description |
---|---|---|
dimension | size_t | Dimensionality of vector embeddings. |
n_lists | size_t | Number of inverted index lists to create in the index (recommended base-2 value). |
metric | DistanceMetric | (Optional) Distance metric to use for index build and queries. |
Speed | Recall | Index Size |
---|---|---|
Fast | Highest | Biggest |
Parameter | Type | Description |
---|---|---|
dimension | size_t | Dimensionality of vector embeddings. |
n_lists | size_t | Number of inverted index lists to create in the index (recommended base-2 value). |
metric | DistanceMetric | (Optional) Distance metric to use for index build and queries. |
Speed | Recall | Index Size |
---|---|---|
Fast | High | Medium |
Parameter | Type | Description |
---|---|---|
dimension | size_t | Dimensionality of vector embeddings. |
n_lists | size_t | Number of inverted index lists to create in the index (recommended base-2 value). |
pq_dim | size_t | Dimensionality of embeddings after quantization (less than or equal to dimension ). |
pq_bits | size_t | Number of bits per dimension for PQ embeddings (between 1 and 16). |
metric | DistanceMetric | (Optional) Distance metric to use for index build and queries. |
Array2D
class provides a 2D container for data, which can be initialized with a specific number of rows and columns, or from an existing vector.
Array2D(size_t rows, size_t cols, const T& initial_value = T())
: Creates an empty 2D array with specified dimensions.Array2D(std::vector<T>&& data, size_t cols)
: Initializes the 2D array from a 1D vector.Array2D(const std::vector<T>& data, size_t cols)
: Initializes the 2D array from a 1D vector (copy).operator()(size_t row, size_t col) const
: Access an element at the specified row and column (read-only).operator()(size_t row, size_t col)
: Access an element at the specified row and column (read-write).size_t rows() const
: Returns the number of rows.size_t cols() const
: Returns the number of columns.size_t size() const
: Returns the total number of elements.TrainingConfig
struct defines parameters for training an index, allowing control over convergence and memory usage.
Parameter | Type | Description |
---|---|---|
batch_size | size_t | (Optional) Size of each batch for training. Defaults to 0 , which auto-selects the batch size. |
max_iters | size_t | (Optional) Maximum iterations for training. Defaults to 0 , which auto-selects iterations. |
tolerance | double | (Optional) Convergence tolerance for training. Defaults to 1e-6 . |
max_memory | size_t | (Optional) Maximum memory (MB) usage during training. Defaults to 0 , no limit. |
QueryParams
struct defines parameters for querying the index, controlling the number of results and probing behavior.
Parameter | Type | Description |
---|---|---|
top_k | size_t | (Optional) Number of nearest neighbors to return. Defaults to 100 . |
n_probes | size_t | (Optional) Number of lists to probe during query. Defaults to 1 . |
filters | std::string | (Optional) A JSON string of filters to apply to vector metadata, limiting search scope to these vectors. |
include | std::vector<ResultFields> | (Optional) List of result fields to return. Can include kDistance and kMetadata . Defaults to empty. |
greedy | bool | (Optional) Whether to perform greedy search. Defaults to false . |
filters
use a subset of the MongoDB Query and Projection Operators.
For instance: filters: { "$and": [ { "label": "cat" }, { "confidence": { "$gte": 0.9 } } ] }
means that only vectors where label == "cat"
and confidence >= 0.9
will be considered for encrypted vector search.
For more info on metadata, see Metadata Filtering.QueryResults
class holds the results from a Query
operation, including IDs and distances for the nearest neighbors of each query.
Method | Return Type | Description |
---|---|---|
Result operator[](size_t query_idx) | Result | Returns read-write access to IDs and distances for a specific query. |
const std::vector<std::vector<std::string>>& ids() const | std::vector<std::vector<std::string>>& | Get read-only access to all IDs. |
const Array2D<float>& distances() const | const Array2D<float>& | Get read-only access to all distances. |
const std::vector<float>& vectors() const | const std::vectorfloat>& | Get read-only access to all vectors. |
const std::vector<std::vector<std::string>>& metadatas() const | const std::vector<std::vector<std::string>>& | Get read-only access to all metadatas. |
size_t num_queries() const | size_t | Returns the number of queries. |
size_t top_k() const | size_t | Returns the number of top-k items per query. |
bool empty() const | bool | Checks if the results are empty. |
Item
Item
struct holds the individual results from a Get
operation, including the requested fields.
ResultFields
enum specifies which fields to include in query results.
ItemFields
enum defines the fields that can be requested for an Item
object.
ids
are always included in the returned items.