Python bindings for NMSLIB
====================================

Installation
------------

This project works with Python on version 2.7+ and 3.5+, and on Linux, OSX and the Windows operating systems. To install:

``pip install nmslib``

This command will attempt to install a pre-compiled binary, which can be a bit slower.
For best performance, the library needs to be installed from sources:

``pip install --no-binary :all: nmslib``

For installation from sources, you may need to install Python dev-files. On Ubuntu, you can do it as follows:

``sudo apt-get install python3-dev``

Building on Windows requires Visual Studio 2015, see `this project for more information <https://github.com/pybind/python_example#installation>`_.

Notes about issues with deploying version 2.0.x
-------------

We unfortunately had deployment issues. As a result we had to delete several versions between 2.0.6 and 2.1.1. If you installed one of these versions, please, delete them and install a more recent version (>=2.1.1).


Example Usage
-------------

.. code-block:: python

    import nmslib
    import numpy

    # create a random matrix to index
    data = numpy.random.randn(10000, 100).astype(numpy.float32)

    # initialize a new index, using a HNSW index on Cosine Similarity
    index = nmslib.init(method='hnsw', space='cosinesimil')
    index.addDataPointBatch(data)
    index.createIndex({'post': 2}, print_progress=True)

    # query for the nearest neighbours of the first datapoint
    ids, distances = index.knnQuery(data[0], k=10)

    # get all nearest neighbours for all the datapoint
    # using a pool of 4 threads to compute
    neighbours = index.knnQueryBatch(data, k=10, num_threads=4)