Struct faiss::IndexNSG

struct IndexNSG : public faiss::Index

The NSG index is a normal random-access index with a NSG link structure built on top

Subclassed by faiss::IndexNSGFlat, faiss::IndexNSGPQ, faiss::IndexNSGSQ

Public Types

using component_t = float
using distance_t = float

Public Functions

explicit IndexNSG(int d = 0, int R = 32, MetricType metric = METRIC_L2)
explicit IndexNSG(Index *storage, int R = 32)
~IndexNSG() override
void build(idx_t n, const float *x, idx_t *knn_graph, int GK)
virtual void add(idx_t n, const float *x) override

Add n vectors of dimension d to the index.

Vectors are implicitly assigned labels ntotal .. ntotal + n - 1 This function slices the input vectors in chunks smaller than blocksize_add and calls add_core.

Parameters:

  • n – number of vectors

  • x – input matrix, size n * d

virtual void train(idx_t n, const float *x) override

Trains the storage if needed.

virtual void search(idx_t n, const float *x, idx_t k, float *distances, idx_t *labels, const SearchParameters *params = nullptr) const override

entry point for search

virtual void reconstruct(idx_t key, float *recons) const override

Reconstruct a stored vector (or an approximation if lossy coding)

this function may not be defined for some indexes

Parameters:

  • key – id of the vector to reconstruct

  • recons – reconstucted vector (size d)

virtual void reset() override

removes all elements from the database.

void check_knn_graph(const idx_t *knn_graph, idx_t n, int K) const
virtual void add_with_ids(idx_t n, const float *x, const idx_t *xids)

Same as add, but stores xids instead of sequential ids.

The default implementation fails with an assertion, as it is not supported by all indexes.

Parameters:

  • n – number of vectors

  • x – input vectors, size n * d

  • xids – if non-null, ids to store for the vectors (size n)

virtual void range_search(idx_t n, const float *x, float radius, RangeSearchResult *result, const SearchParameters *params = nullptr) const

query n vectors of dimension d to the index.

return all vectors with distance < radius. Note that many indexes do not implement the range_search (only the k-NN search is mandatory).

Parameters:

  • n – number of vectors

  • x – input vectors to search, size n * d

  • radius – search radius

  • result – result table

virtual void assign(idx_t n, const float *x, idx_t *labels, idx_t k = 1) const

return the indexes of the k vectors closest to the query x.

This function is identical as search but only return labels of neighbors.

Parameters:

  • n – number of vectors

  • x – input vectors to search, size n * d

  • labels – output labels of the NNs, size n*k

  • k – number of nearest neighbours

virtual size_t remove_ids(const IDSelector &sel)

removes IDs from the index. Not supported by all indexes. Returns the number of elements removed.

virtual void reconstruct_batch(idx_t n, const idx_t *keys, float *recons) const

Reconstruct several stored vectors (or an approximation if lossy coding)

this function may not be defined for some indexes

Parameters:

  • n – number of vectors to reconstruct

  • keys – ids of the vectors to reconstruct (size n)

  • recons – reconstucted vector (size n * d)

virtual void reconstruct_n(idx_t i0, idx_t ni, float *recons) const

Reconstruct vectors i0 to i0 + ni - 1

this function may not be defined for some indexes

Parameters:

  • i0 – index of the first vector in the sequence

  • ni – number of vectors in the sequence

  • recons – reconstucted vector (size ni * d)

virtual void search_and_reconstruct(idx_t n, const float *x, idx_t k, float *distances, idx_t *labels, float *recons, const SearchParameters *params = nullptr) const

Similar to search, but also reconstructs the stored vectors (or an approximation in the case of lossy coding) for the search results.

If there are not enough results for a query, the resulting arrays is padded with -1s.

Parameters:

  • n – number of vectors

  • x – input vectors to search, size n * d

  • k – number of extracted vectors

  • distances – output pairwise distances, size n*k

  • labels – output labels of the NNs, size n*k

  • recons – reconstructed vectors size (n, k, d)

virtual void compute_residual(const float *x, float *residual, idx_t key) const

Computes a residual vector after indexing encoding.

The residual vector is the difference between a vector and the reconstruction that can be decoded from its representation in the index. The residual can be used for multiple-stage indexing methods, like IndexIVF’s methods.

Parameters:

  • x – input vector, size d

  • residual – output residual vector, size d

  • key – encoded index, as returned by search and assign

virtual void compute_residual_n(idx_t n, const float *xs, float *residuals, const idx_t *keys) const

Computes a residual vector after indexing encoding (batch form). Equivalent to calling compute_residual for each vector.

The residual vector is the difference between a vector and the reconstruction that can be decoded from its representation in the index. The residual can be used for multiple-stage indexing methods, like IndexIVF’s methods.

Parameters:

  • n – number of vectors

  • xs – input vectors, size (n x d)

  • residuals – output residual vectors, size (n x d)

  • keys – encoded index, as returned by search and assign

virtual DistanceComputer *get_distance_computer() const

Get a DistanceComputer (defined in AuxIndexStructures) object for this kind of index.

DistanceComputer is implemented for indexes that support random access of their vectors.

virtual size_t sa_code_size() const

size of the produced codes in bytes

virtual void sa_encode(idx_t n, const float *x, uint8_t *bytes) const

encode a set of vectors

Parameters:

  • n – number of vectors

  • x – input vectors, size n * d

  • bytes – output encoded vectors, size n * sa_code_size()

virtual void sa_decode(idx_t n, const uint8_t *bytes, float *x) const

decode a set of vectors

Parameters:

  • n – number of vectors

  • bytes – input encoded vectors, size n * sa_code_size()

  • x – output vectors, size n * d

virtual void merge_from(Index &otherIndex, idx_t add_id = 0)

moves the entries from another dataset to self. On output, other is empty. add_id is added to all moved ids (for sequential ids, this would be this->ntotal)

virtual void check_compatible_for_merge(const Index &otherIndex) const

check that the two indexes are compatible (ie, they are trained in the same way and have the same parameters). Otherwise throw.

virtual void add_sa_codes(idx_t n, const uint8_t *codes, const idx_t *xids)

Add vectors that are computed with the standalone codec

Parameters:

  • codes – codes to add size n * sa_code_size()

  • xids – corresponding ids, size n

Public Members

NSG nsg

the link structure

bool own_fields = false

the sequential storage

Index *storage = nullptr
bool is_built = false

the index is built or not

int GK = 64

K of KNN graph for building.

char build_type = 0

indicate how to build a knn graph

int nndescent_S = 10

parameters for nndescent

int nndescent_R = 100
int nndescent_L
int nndescent_iter = 10
int d

vector dimension

idx_t ntotal

total nb of indexed vectors

bool verbose

verbosity level

bool is_trained

set if the Index does not require training, or if training is done already

MetricType metric_type

type of metric this index uses for search

float metric_arg

argument of the metric type