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https://issues.apache.org/jira/browse/LUCENE-9004?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17032966#comment-17032966
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Julie Tibshirani commented on LUCENE-9004:
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As another option for running benchmarks I wanted to give more information on
the [ann-benchmarks repo](https://github.com/erikbern/ann-benchmarks). It’s a
shared set of benchmarks developed by the kNN search community, and contains a
set of pretty realistic datasets, as well as connectors to existing kNN
libraries like FAISS.
I pushed a branch that hooks up the Lucene HNSW prototype to ann-benchmarks:
https://github.com/jtibshirani/ann-benchmarks/pull/1. It’s nice to have
everything in one place, as we can compare prototypes against reference
implementations from FAISS to check that the recalls match. [This
comment](https://github.com/jtibshirani/ann-benchmarks/pull/1#issuecomment-583760337)
contains results of running both the HNSW prototype and FAISS’s implementation
against a small test dataset. It looks like the prototype gives ~5% lower
recall for the same parameter values, which suggests there’s room for small
fixes/ improvements in terms of the algorithm. (I might have misunderstood the
default parameter values though, any corrections are welcome!)
Some more background:
* That test uses a small test dataset because I had trouble getting
`forceMerge(1)` to complete on a large dataset. But there are some more
realistic datasets like `glove-100-angular` (a set of 1.2 million GloVe word
vectors), and `deep-image-96-angular` (a set of 10M ‘deep descriptors’ of
images from a CNN).
* By default, ann-benchmarks retrieves k=10 nearest neighbors, and reports
recall as the number of results that overlap with the true k nearest neighbors.
There is an adjustable small ‘fudge factor’ epsilon, so that a result is still
counted as correct if it is within a small distance of the true kth nearest
neighbor.
* Since ann-benchmarks is a Python library, the branch uses py4j to convert to
and from Java. py4j could add non-trivial overhead, so this benchmarking
strategy is probably not best for measuring raw QPS. But it can be useful to
(1) examine recall numbers, and (2) compare different Lucene kNN approaches
against each other.
Feel free to ping me/ comment on the PR if you spot issues or have trouble
getting it to work.
> 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: 3h 10m
> 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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