Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs · HackerPapers discussions

Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs — mentioned by seanhunter

Thu, 05 Sep 2024 03:33:16 GMT

This could of course be a bug, but it's worth noting that vector searching in general is a semantic ("meaning based") search technique, but it doesn't operate on the actual text, it operates on the resulting embedding so it may well make sense to include a (possibly fuzzy) full text search as well to catch literal instances of the text if that's what you actually need. That said, the particular example you give may be either a configuration problem or just that "cat" is a particularly bad choice of word to use for a vector search in your corpus. HNSW[1] is an approximate nearest neighbour…

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Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs — mentioned by bazzargh

Wed, 10 May 2023 16:02:32 GMT

the Hamming distance in the article is about twice the number of moves to get from one position to the other, mate in 4 is important? A better criticism might be: this metric defines position as close even if can only be reached by reversing moves. There's some discussion in the HNSW paper https://arxiv.org/pdf/1603.09320.pdf of working with non-symmetric metrics but I haven't read further.

Paper and discussion history. A mention is not an endorsement.

Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs — mentioned by binarymax

Thu, 21 Jul 2022 23:59:33 GMT

HNSW, or Hierarchical Navigable Small World is a graph data structure for approximate nearest neighbor search of vectors. https://arxiv.org/abs/1603.09320 The problem space of ANN is one of those really deep holes you can go down. It’s a game of balancing time and space, and it’s got plenty of fascinating algorithms and datastructures. Check out http://ann-benchmarks.com/ for a comparison. HNSW is not “the best” but it’s easy to understand and is quite effective.

Paper and discussion history. A mention is not an endorsement.