In the inverted file, the quantizer (an Index instance) provides a
quantization index for each vector to be added. The quantization
index maps to a list (aka inverted list or posting list), where the
id of the vector is then stored.
At search time, the vector to be searched is also quantized, and
only the list corresponding to the quantization index is
searched. This speeds up the search by making it
non-exhaustive. This can be relaxed using multi-probe search: a few
(nprobe) quantization indices are selected and several inverted
lists are visited.
Sub-classes implement a post-filtering of the index that refines
the distance estimation from the query to database vectors.
Index based on a inverted file (IVF)
In the inverted file, the quantizer (an Index instance) provides a quantization index for each vector to be added. The quantization index maps to a list (aka inverted list or posting list), where the id of the vector is then stored.
At search time, the vector to be searched is also quantized, and only the list corresponding to the quantization index is searched. This speeds up the search by making it non-exhaustive. This can be relaxed using multi-probe search: a few (nprobe) quantization indices are selected and several inverted lists are visited.
Sub-classes implement a post-filtering of the index that refines the distance estimation from the query to database vectors.