|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
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|Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk|
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|Awesome Vector Search||1,031||6 days ago||13||mit|
|Collections of vector search related libraries, service and research papers|
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|Vector database plugin for Postgres, written in Rust, specifically designed for LLM|
|Lopq||512||4 years ago||3||September 22, 2016||15||apache-2.0||Python|
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|Elastiknn||340||2||18 days ago||29||September 27, 2021||22||apache-2.0||Scala|
|Elasticsearch plugin for nearest neighbor search. Store vectors and run similarity search using exact and approximate algorithms.|
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|An easy to use Neural Search Engine. Index latent vectors along with JSON metadata and do efficient k-NN search.|
Annoy (Approximate Nearest Neighbors Oh Yeah) is a C++ library with Python bindings to search for points in space that are close to a given query point. It also creates large read-only file-based data structures that are mmapped into memory so that many processes may share the same data.
To install, simply do
pip install --user annoy to pull down the latest version from PyPI.
For the C++ version, just clone the repo and
There are some other libraries to do nearest neighbor search. Annoy is almost as fast as the fastest libraries, (see below), but there is actually another feature that really sets Annoy apart: it has the ability to use static files as indexes. In particular, this means you can share index across processes. Annoy also decouples creating indexes from loading them, so you can pass around indexes as files and map them into memory quickly. Another nice thing of Annoy is that it tries to minimize memory footprint so the indexes are quite small.
Why is this useful? If you want to find nearest neighbors and you have many CPU's, you only need to build the index once. You can also pass around and distribute static files to use in production environment, in Hadoop jobs, etc. Any process will be able to load (mmap) the index into memory and will be able to do lookups immediately.
We use it at Spotify for music recommendations. After running matrix factorization algorithms, every user/item can be represented as a vector in f-dimensional space. This library helps us search for similar users/items. We have many millions of tracks in a high-dimensional space, so memory usage is a prime concern.
from annoy import AnnoyIndex import random f = 40 # Length of item vector that will be indexed t = AnnoyIndex(f, 'angular') for i in range(1000): v = [random.gauss(0, 1) for z in range(f)] t.add_item(i, v) t.build(10) # 10 trees t.save('test.ann') # ... u = AnnoyIndex(f, 'angular') u.load('test.ann') # super fast, will just mmap the file print(u.get_nns_by_item(0, 1000)) # will find the 1000 nearest neighbors
Right now it only accepts integers as identifiers for items. Note that it will allocate memory for max(id)+1 items because it assumes your items are numbered 0 n-1. If you need other id's, you will have to keep track of a map yourself.
AnnoyIndex(f, metric)returns a new index that's read-write and stores vector of
fdimensions. Metric can be
a.add_item(i, v)adds item
i(any nonnegative integer) with vector
v. Note that it will allocate memory for
a.build(n_trees, n_jobs=-1)builds a forest of
n_treestrees. More trees gives higher precision when querying. After calling
build, no more items can be added.
n_jobsspecifies the number of threads used to build the trees.
n_jobs=-1uses all available CPU cores.
a.save(fn, prefault=False)saves the index to disk and loads it (see next function). After saving, no more items can be added.
a.load(fn, prefault=False)loads (mmaps) an index from disk. If prefault is set to True, it will pre-read the entire file into memory (using mmap with MAP_POPULATE). Default is False.
a.get_nns_by_item(i, n, search_k=-1, include_distances=False)returns the
nclosest items. During the query it will inspect up to
search_knodes which defaults to
n_trees * nif not provided.
search_kgives you a run-time tradeoff between better accuracy and speed. If you set
True, it will return a 2 element tuple with two lists in it: the second one containing all corresponding distances.
a.get_nns_by_vector(v, n, search_k=-1, include_distances=False)same but query by vector
a.get_item_vector(i)returns the vector for item
ithat was previously added.
a.get_distance(i, j)returns the distance between items
j. NOTE: this used to return the squared distance, but has been changed as of Aug 2016.
a.get_n_items()returns the number of items in the index.
a.get_n_trees()returns the number of trees in the index.
a.on_disk_build(fn)prepares annoy to build the index in the specified file instead of RAM (execute before adding items, no need to save after build)
a.set_seed(seed)will initialize the random number generator with the given seed. Only used for building up the tree, i. e. only necessary to pass this before adding the items. Will have no effect after calling a.build(n_trees) or a.load(fn).
The C++ API is very similar: just
#include "annoylib.h" to get access to it.
There are just two main parameters needed to tune Annoy: the number of trees
n_trees and the number of nodes to inspect during searching
n_treesis provided during build time and affects the build time and the index size. A larger value will give more accurate results, but larger indexes.
search_kis provided in runtime and affects the search performance. A larger value will give more accurate results, but will take longer time to return.
search_k is not provided, it will default to
n * n_trees where
n is the number of approximate nearest neighbors. Otherwise,
n_trees are roughly independent, i.e. the value of
n_trees will not affect search time if
search_k is held constant and vice versa. Basically it's recommended to set
n_trees as large as possible given the amount of memory you can afford, and it's recommended to set
search_k as large as possible given the time constraints you have for the queries.
You can also accept slower search times in favour of reduced loading times, memory usage, and disk IO. On supported platforms the index is prefaulted during
save, causing the file to be pre-emptively read from disk into memory. If you set
False, pages of the mmapped index are instead read from disk and cached in memory on-demand, as necessary for a search to complete. This can significantly increase early search times but may be better suited for systems with low memory compared to index size, when few queries are executed against a loaded index, and/or when large areas of the index are unlikely to be relevant to search queries.
Using random projections and by building up a tree. At every intermediate node in the tree, a random hyperplane is chosen, which divides the space into two subspaces. This hyperplane is chosen by sampling two points from the subset and taking the hyperplane equidistant from them.
We do this k times so that we get a forest of trees. k has to be tuned to your need, by looking at what tradeoff you have between precision and performance.
Hamming distance (contributed by Martin Aumller) packs the data into 64-bit integers under the hood and uses built-in bit count primitives so it could be quite fast. All splits are axis-aligned.
Dot Product distance (contributed by Peter Sobot and Pavel Korobov) reduces the provided vectors from dot (or "inner-product") space to a more query-friendly cosine space using a method by Bachrach et al., at Microsoft Research, published in 2014.
It's all written in C++ with a handful of ugly optimizations for performance and memory usage. You have been warned :)
To run the tests, execute python setup.py nosetests. The test suite includes a big real world dataset that is downloaded from the internet, so it will take a few minutes to execute.