File Index.h

Defines

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Copyright (c) Facebook, Inc. and its affiliates.

This source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree.

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namespace faiss

Copyright (c) Facebook, Inc. and its affiliates.

This source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree.

Copyright (c) Facebook, Inc. and its affiliates.

This source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree. Implementation of k-means clustering with many variants.

Copyright (c) Facebook, Inc. and its affiliates.

This source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree. IDSelector is intended to define a subset of vectors to handle (for removal or as subset to search)

Copyright (c) Facebook, Inc. and its affiliates.

This source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree. PQ4 SIMD packing and accumulation functions

The basic kernel accumulates nq query vectors with bbs = nb * 2 * 16 vectors and produces an output matrix for that. It is interesting for nq * nb <= 4, otherwise register spilling becomes too large.

The implementation of these functions is spread over 3 cpp files to reduce parallel compile times. Templates are instantiated explicitly.

Copyright (c) Facebook, Inc. and its affiliates.

This source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree. This file contains callbacks for kernels that compute distances.

Throughout the library, vectors are provided as float * pointers. Most algorithms can be optimized when several vectors are processed (added/searched) together in a batch. In this case, they are passed in as a matrix. When n vectors of size d are provided as float * x, component j of vector i is

x[ i * d + j ]

where 0 <= i < n and 0 <= j < d. In other words, matrices are always compact. When specifying the size of the matrix, we call it an n*d matrix, which implies a row-major storage.

Copyright (c) Facebook, Inc. and its affiliates.

This source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree. I/O functions can read/write to a filename, a file handle or to an object that abstracts the medium.

The read functions return objects that should be deallocated with delete. All references within these objectes are owned by the object.

Copyright (c) Facebook, Inc. and its affiliates.

This source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree. Definition of inverted lists + a few common classes that implement the interface.

Copyright (c) Facebook, Inc. and its affiliates.

This source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree. Since IVF (inverted file) indexes are of so much use for large-scale use cases, we group a few functions related to them in this small library. Most functions work both on IndexIVFs and IndexIVFs embedded within an IndexPreTransform.

Copyright (c) Facebook, Inc. and its affiliates.

This source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree. In this file are the implementations of extra metrics beyond L2 and inner product

Copyright (c) Facebook, Inc. and its affiliates.

This source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree. Defines a few objects that apply transformations to a set of vectors Often these are pre-processing steps.

struct SearchParameters
#include <Index.h>

Parent class for the optional search paramenters.

Sub-classes with additional search parameters should inherit this class. Ownership of the object fields is always to the caller.

Subclassed by faiss::IndexRefineSearchParameters, faiss::SearchParametersHNSW, faiss::SearchParametersIVF, faiss::SearchParametersPQ, faiss::SearchParametersPreTransform, faiss::SearchParametersResidualCoarseQuantizer

Public Functions

inline virtual ~SearchParameters()

make sure we can dynamic_cast this

Public Members

IDSelector *sel = nullptr

if non-null, only these IDs will be considered during search.

struct Index
#include <Index.h>

Abstract structure for an index, supports adding vectors and searching them.

All vectors provided at add or search time are 32-bit float arrays, although the internal representation may vary.

Subclassed by faiss::AdditiveCoarseQuantizer, faiss::IndexFastScan, faiss::IndexFlatCodes, faiss::IndexHNSW, faiss::IndexIVF, faiss::IndexIVFIndependentQuantizer, faiss::IndexLattice, faiss::IndexNNDescent, faiss::IndexNSG, faiss::IndexPreTransform, faiss::IndexRandom, faiss::IndexRefine, faiss::IndexRowwiseMinMaxBase, faiss::IndexSplitVectors, faiss::MultiIndexQuantizer, faiss::gpu::GpuIndex

Public Types

using component_t = float
using distance_t = float

Public Functions

inline explicit Index(idx_t d = 0, MetricType metric = METRIC_L2)
virtual ~Index()
virtual void train(idx_t n, const float *x)

Perform training on a representative set of vectors

Parameters:

  • n – nb of training vectors

  • x – training vecors, size n * d

virtual void add(idx_t n, const float *x) = 0

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 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 search(idx_t n, const float *x, idx_t k, float *distances, idx_t *labels, const SearchParameters *params = nullptr) const = 0

query n vectors of dimension d to the index.

return at most k vectors. If there are not enough results for a query, the result array 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

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 void reset() = 0

removes all elements from the database.

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(idx_t key, float *recons) const

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 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.

Public Members

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