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
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.
Here is a simple example, but we also have Python notebooks with more elaborate end-to-end examples, which include even computation of gold-standard data (for both sparse and dense space):
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)NMSLIB produces quite a few informational messages. By default, they are not shown in Python. To enable debugging, one should use the following commands before importing the library:
import logging
logging.basicConfig(level=logging.DEBUG)
To enable extra methods like those provided by FALCONN and LSHKIT you need to follow an extra couple steps.
These methods require a development version of the following libraries: Boost, GNU scientific library, and Eigen3. To install on Ubuntu:
sudo apt-get install libboost-all-dev libgsl0-dev libeigen3-dev
Next clone the repository and build with the C++ files using CMake:
cd similarity_search
cmake . -DWITH_EXTRAS=1
make
cd ..
Finally build and install the python extension:
cd python_bindings
pip install -r requirements.txt
python setup.py install
More detailed documentation is also available (thanks to Ben Frederickson).