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Package Index > annoy > 1.9.5

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annoy 1.9.5


For the latest source, discussion, etc, please visit the GitHub repository 


To install, simply do sudo pip install annoy to pull down the latest version from PyPI.

For the C++ version, just clone the repo and #include "annoylib.h".


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.

Annoy was built by Erik Bernhardsson in a couple of afternoons during Hack Week.

Summary of features

Python code example

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.

Full Python API


The C++ API is very similar: just #include "annoylib.h" to get access to it.


There are just two parameters you can use to tune Annoy: the number of trees n_trees and the number of nodes to inspect during searching search_k.

If search_k is not provided, it will default to n * n_trees where n is the number of approximate nearest neighbors. Otherwise, search_k and n_trees are roughly independent, i.e. a 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.

How does it work

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.
 Source code

The code should support Windows, thanks to thirdwing.

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.


Feel free to post any questions or comments to the annoy-user group. I’m @fulhack on Twitter.