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https://issues.apache.org/jira/browse/LUCENE-9004?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17019066#comment-17019066
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Michael Sokolov commented on LUCENE-9004:
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I'll second the thanks, [~jtibshirani] . There's clearly active work going on,
and it may be too soon to declare a single winner in this complex space. I do
think there is a need to focus on higher-dimensional cases since in Lucene
there is already well-developed support for dim <=8 via KD-tree, but nothing
for higher dimensions.
One thing that surprises me a bit about some evaluations I'm seeing is that
they report Precision@1 (and sometimes even when operating over the training
set?!). I wonder if anyone has looked at a metric that includes top 10 (say),
and penalizes more distant matches? For exmaple MSE over normalized vectors
would enable one to distinguish among results that are both the same
"precision" yet one has vectors that are closer than the other.
Re: deletions, yeah we have not addressed that. The only thing that makes sense
to me for deletions is to prune them while searching. TBH I'm not sure how to
plumb livedocs in to the query, or if this is somehow untenable? Supposing we
do that, it would impose some operational constraints in that if a lot of
documents are deleted, performance will drop substantially, but I think that is
probably OK. Users will just have to understand the limitation? We'll have to
understand the impact as deletions accumulate.
I think the issue about filtering against other queries is more challenging
since we don't have an up-front bitset to filter against, typically. In a sense
the ANN query is the most expensive because *every* document is a potential
match. Perhaps the thing to do is come up with an estimate of a radius R
bounding the top K (around the query vector) based on the approximate top K we
find, and then allowing to advance to a document, even if it was not returned
by graph search, so long as its distance is <= R. This would not truly answer
the question "top K closest documents satisfying these constraints," though.
For that I don't see what we could do other than forcing to compute a bitset,
and then passing that in to the graph search (like for deletions).
> Approximate nearest vector search
> ---------------------------------
>
> Key: LUCENE-9004
> URL: https://issues.apache.org/jira/browse/LUCENE-9004
> Project: Lucene - Core
> Issue Type: New Feature
> Reporter: Michael Sokolov
> Priority: Major
> Attachments: hnsw_layered_graph.png
>
> Time Spent: 2.5h
> Remaining Estimate: 0h
>
> "Semantic" search based on machine-learned vector "embeddings" representing
> terms, queries and documents is becoming a must-have feature for a modern
> search engine. SOLR-12890 is exploring various approaches to this, including
> providing vector-based scoring functions. This is a spinoff issue from that.
> The idea here is to explore approximate nearest-neighbor search. Researchers
> have found an approach based on navigating a graph that partially encodes the
> nearest neighbor relation at multiple scales can provide accuracy > 95% (as
> compared to exact nearest neighbor calculations) at a reasonable cost. This
> issue will explore implementing HNSW (hierarchical navigable small-world)
> graphs for the purpose of approximate nearest vector search (often referred
> to as KNN or k-nearest-neighbor search).
> At a high level the way this algorithm works is this. First assume you have a
> graph that has a partial encoding of the nearest neighbor relation, with some
> short and some long-distance links. If this graph is built in the right way
> (has the hierarchical navigable small world property), then you can
> efficiently traverse it to find nearest neighbors (approximately) in log N
> time where N is the number of nodes in the graph. I believe this idea was
> pioneered in [1]. The great insight in that paper is that if you use the
> graph search algorithm to find the K nearest neighbors of a new document
> while indexing, and then link those neighbors (undirectedly, ie both ways) to
> the new document, then the graph that emerges will have the desired
> properties.
> The implementation I propose for Lucene is as follows. We need two new data
> structures to encode the vectors and the graph. We can encode vectors using a
> light wrapper around {{BinaryDocValues}} (we also want to encode the vector
> dimension and have efficient conversion from bytes to floats). For the graph
> we can use {{SortedNumericDocValues}} where the values we encode are the
> docids of the related documents. Encoding the interdocument relations using
> docids directly will make it relatively fast to traverse the graph since we
> won't need to lookup through an id-field indirection. This choice limits us
> to building a graph-per-segment since it would be impractical to maintain a
> global graph for the whole index in the face of segment merges. However
> graph-per-segment is a very natural at search time - we can traverse each
> segments' graph independently and merge results as we do today for term-based
> search.
> At index time, however, merging graphs is somewhat challenging. While
> indexing we build a graph incrementally, performing searches to construct
> links among neighbors. When merging segments we must construct a new graph
> containing elements of all the merged segments. Ideally we would somehow
> preserve the work done when building the initial graphs, but at least as a
> start I'd propose we construct a new graph from scratch when merging. The
> process is going to be limited, at least initially, to graphs that can fit
> in RAM since we require random access to the entire graph while constructing
> it: In order to add links bidirectionally we must continually update existing
> documents.
> I think we want to express this API to users as a single joint
> {{KnnGraphField}} abstraction that joins together the vectors and the graph
> as a single joint field type. Mostly it just looks like a vector-valued
> field, but has this graph attached to it.
> I'll push a branch with my POC and would love to hear comments. It has many
> nocommits, basic design is not really set, there is no Query implementation
> and no integration iwth IndexSearcher, but it does work by some measure using
> a standalone test class. I've tested with uniform random vectors and on my
> laptop indexed 10K documents in around 10 seconds and searched them at 95%
> recall (compared with exact nearest-neighbor baseline) at around 250 QPS. I
> haven't made any attempt to use multithreaded search for this, but it is
> amenable to per-segment concurrency.
> [1]
> [https://www.semanticscholar.org/paper/Efficient-and-robust-approximate-nearest-neighbor-Malkov-Yashunin/699a2e3b653c69aff5cf7a9923793b974f8ca164]
>
> *UPDATES:*
> * (1/12/2020) The up-to-date branch is:
> [https://github.com/apache/lucene-solr/tree/jira/lucene-9004-aknn-2]
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