Special operations on indexes

There are a few additional operations that are not supported for all types of indexes.

Database operations on indexes.

These are operations that rely on id-based access on the dataset.

Direct Map for an `IndexIVF`

The vector ids for an IndexIVF (and IndexBinaryIVF) are stored in the inverted lists. Therefore there is no way to map back from an id to the entry in the index. To support removal or updates on IndexIVF, the DirectMap field of the IndexIVF object stores a mapping from id to the location where it is stored in the index. It can have 3 values:

DirectMap.NoMap: no mapping is stored, reconstruction is not possible (default).
DirectMap.Array: the direct map is an array. The indices are assumed to be sequential, which rules out add_with_ids
DirectMap.Hashtable: the direct map is a hashtable. Indices can be arbitrary and add_with_ids works (provided indices are distinct).

Set or change the DirectMap type with index.set_direct_map_type(DirectMap.Array). Example usage here: TestReconsHash.

Reconstructing one vector or a subset

One vector can be reconstructed with the reconstruct method. To call it on several vectors (mainly useful in python to avoid the loop), use reconstruct_batch.

Reconstructing all vectors of a range: `reconstruct_n`

This function reconstructs all vectors from a range of contiguous ids. ids that are not present in the dataset are untouched on output.

Reconstruct some vectors

Example usage: test_index_composite.py

This is supported for all types of indexes. Make sure to enable the direct map for the IndexIVF index types.

Removing elements from an index

The method remove_ids removes a subset of vectors from an index. It takes an IDSelector object that is called for every element in the index to decide whether it should be removed. IDSelectorBatch will do this for a list of indices. The Python interface constructs this from numpy arrays if necessary.

NB that since it does a pass over the whole database, this is efficient only when a significant number of vectors needs to be removed (see exception below).

Example: test_index_composite.py

Supported by IndexFlat, IndexIVFFlat, IDMap.

Note that there is a semantic difference when removing ids from sequential indexes vs. when removing them from an IndexIVF:

for sequential indexes (IndexFlat, IndexPQ, IndexLSH), the removal operation shifts the ids of vectors above the removed vector id.
the IndexIVF and IndexIDMap2 store the ids of vectors explicitly, so the ids of other vectors are not changed. There are two special cases for IndexIVF:
- DirectMap type Array does not support removal because it means that all the indices would be shifted, which does not seem very useful.
- with a direct map type Hashtable and a selector IDSelectorArray elements can be removed without scanning the whole index.

Range search

The method range_search returns all vectors within a radius around the query point (as opposed to the k nearest ones). Since the result lists for each query are of different sizes, it must be handled specially:

in C++ it returns the results in a pre-allocated RangeSearchResult structure
in Python, the results are returned as a triplet of 1D arrays lims, D, I, where result for query i is in I[lims[i]:lims[i+1]] (indices of neighbors), D[lims[i]:lims[i+1]] (distances).

Supported by (CPU only): IndexFlat, IndexIVFFlat, IndexScalarQuantizer, IndexIVFScalarQuantizer.

Splitting and merging indexes

The methods:

merge_from copies another index to this and deallocates it on-the-fly. You can use ivflib::merge_into for IndexIVFs wrapped in a pre-transform.
copy_subset_to copies a subset of this codes to another index. Example usage: to build indexes on a GPU and move them to CPU afterwards

The functions are implemented only for IndexIVF subclasses because they are mainly interesting for large indexes.