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https://issues.apache.org/jira/browse/LUCENE-9004?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17017696#comment-17017696
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Julie Tibshirani edited comment on LUCENE-9004 at 1/17/20 4:36 AM:
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Hello and thank you for this very exciting work! We have been doing research
into nearest neighbor search on high-dimensional vectors and I wanted to share
some thoughts here in the hope that they're helpful.
Related to Adrien's comment about search filters, I am wondering how deleted
documents would be handled. If I'm understanding correctly, a segment's deletes
are applied 'on top of' the query. So if the k nearest neighbors to the query
vector all happen to be deleted, then the query won't bring back any documents.
From a user's perspective, I could see this behavior being surprising or hard
to work with. One approach would be to keep expanding the search while skipping
over deleted documents, but I'm not sure about the performance + accuracy it
would give (there's a [short
discussion|https://github.com/nmslib/hnswlib/issues/4#issuecomment-378739892]
in the hnswlib repo on this point).
The recent paper [Graph based Nearest Neighbor Search: Promises and
Failures|https://arxiv.org/abs/1904.02077] compares HNSW to other graph-based
approaches and claims that the hierarchy of layers only really helps in low
dimensions (Figure 4). In these experiments, they see that a 'flat' version of
HNSW performs very similarly to the original above around 16 dimensions. The
original HNSW paper also cites the hierarchy as most helpful in low dimensions.
This seemed interesting in that it may be possible to avoid some complexity if
the focus is not on low-dimensional vectors. (It also suggests that graph-based
kNN is an active research area and that there are likely to be improvements +
new approaches that come out. One such new approach is [DiskANN Fast Accurate
Billion-point Nearest Neighbor Search on a Single
Node|https://suhasjs.github.io/files/diskann_neurips19.pdf]).
On the subject of testing recall, we are working on adding [sentence
embedding|https://github.com/erikbern/ann-benchmarks/issues/144] and [deep
image descriptor|https://github.com/erikbern/ann-benchmarks/issues/143]
datasets to the ann-benchmarks repo. Hopefully that will help provide some
realistic shared data to test against.
was (Author: jtibshirani):
Hello and thank you for this very exciting work! We have been doing research
into nearest neighbor search on high-dimensional vectors and I wanted to share
some thoughts here in the hope that they're helpful.
Related to Adrien's comment about search filters, I am wondering how deleted
documents would be handled. If I'm understanding correctly, a segment's deletes
are applied 'on top of' the query. So if the k nearest neighbors to the query
vector all happen to be deleted, then the query won't bring back any documents.
From a user's perspective, I could see this behavior being surprising or hard
to work with. One approach would be to keep expanding the search while skipping
over deleted documents, but I'm not sure about the performance + accuracy it
would give (there's a [short
discussion|https://github.com/nmslib/hnswlib/issues/4#issuecomment-378739892]
in the hnswlib repo on this point).
The recent paper [Graph based Nearest Neighbor Search: Promises and
Failures|https://arxiv.org/abs/1904.02077] compares HNSW to other graph-based
approaches and claims that the hierarchy of layers only really helps in low
dimensions (Figure 4). In these experiments, they see that a 'flat' version of
HNSW performs very similarly to the original above around 16 dimensions. The
original HNSW paper also cites the hierarchy as most helpful in low dimensions.
This seemed interesting in that it may be possible to avoid some complexity if
the focus is not on low-dimensional vectors. (It also suggests that graph-based
kNN is an active research area and that there are likely to be improvements +
new approaches that come out. One such new approach is [DiskANN: Fast Accurate
Billion-point Nearest Neighbor Search on a Single
Node|[https://suhasjs.github.io/files/diskann_neurips19.pdf]]).
On the subject of testing recall, we are working on adding [sentence
embedding|https://github.com/erikbern/ann-benchmarks/issues/144] and [deep
image descriptor|https://github.com/erikbern/ann-benchmarks/issues/143]
datasets to the ann-benchmarks repo. Hopefully that will help provide some
realistic shared data to test against.
> 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: 40m
> 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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