Adding Python docs

README.md

@@ -40,11 +40,11 @@ As of **May 2016** results are:
 <tr width="100%" border="0" style="border:none">
 <td border="0" align="center" style="border:none">
 1.19M 100d GloVe, cosine similarity.
-<img src="https://raw.githubusercontent.com/searchivarius/nmslib/master/docs/figures/glove.png" width="400">
+<img src="https://raw.githubusercontent.com/searchivarius/nmslib/master/manual/figures/glove.png" width="400">
 </td>
 <td border="0"  align="center" style="border:none">
 1M 128d SIFT features, Euclidean distance:
-<img src="https://raw.githubusercontent.com/searchivarius/nmslib/master/docs/figures/sift.png" width="400">
+<img src="https://raw.githubusercontent.com/searchivarius/nmslib/master/manual/figures/sift.png" width="400">
 </td>
 </tr></table>
 
@@ -61,7 +61,7 @@ What's new in version 1.6 ([see this page for more details](https://github.com/s
 General information
 -----------------------
 
-A detailed description is given [in the manual](docs/manual.pdf). The manual also contains instructions for building under Linux and Windows, extending the library, as well as for debugging the code using Eclipse. Note that the manual is not fully updated to reflect 1.6 changes.
+A detailed description is given [in the manual](manual/manual.pdf). The manual also contains instructions for building under Linux and Windows, extending the library, as well as for debugging the code using Eclipse. Note that the manual is not fully updated to reflect 1.6 changes.
 
 Most of this code is released under the
 Apache License Version 2.0 http://www.apache.org/licenses/.
@@ -109,7 +109,7 @@ cmake . -DWITH_EXTRAS=1
 make  
 ```
 
-Note that the directory **similarity_search** contains an Eclipse project that can be imported into [The Eclipse IDE for C/C++ Developers](http://www.eclipse.org/ide/).  A more detailed description is given in [in the manual](docs/manual.pdf), which also contains examples of using the software.  
+Note that the directory **similarity_search** contains an Eclipse project that can be imported into [The Eclipse IDE for C/C++ Developers](http://www.eclipse.org/ide/).  A more detailed description is given in [in the manual](manual/manual.pdf), which also contains examples of using the software.  
 
 You can also download almost every data set used in our previous evaluations (see the section **Data sets** below). The downloaded data needs to be decompressed (you may need 7z, gzip, and bzip2). Old experimental scripts can be found in the directory [previous_releases_scripts](previous_releases_scripts). However, they will work only with previous releases.
 
@@ -118,7 +118,7 @@ Note that the benchmarking utility **supports caching of ground truth data**, so
 Query server (Linux-only)
 -----------------------
 The query server requires Apache Thrift. We used Apache Thrift 0.9.2, but, perhaps, newer versions will work as well.  
-To install Apache Thrift, you need to [build it from source](https://thrift.apache.org/docs/BuildingFromSource).
+To install Apache Thrift, you need to [build it from source](https://thrift.apache.org/manual/BuildingFromSource).
 This may require additional libraries. On Ubuntu they can be installed as follows:
 ```
 sudo apt-get install libboost-dev libboost-test-dev libboost-program-options-dev libboost-system-dev libboost-filesystem-dev libevent-dev automake libtool flex bison pkg-config g++ libssl-dev libboost-thread-dev make
@@ -168,7 +168,7 @@ Data sets
 We use several data sets, which were created either by other folks,
 or using 3d party software. If you use these data sets, please, consider
 giving proper credit. The download scripts prints respective BibTex entries.
-More information can be found [in the manual](docs/manual.pdf).
+More information can be found [in the manual](manual/manual.pdf).
 
 Here is the list of scripts to download major data sets:
 * Data sets for our NIPS'13 and SISAP'13 papers [data/get_data_nips2013.sh](data/get_data_nips2013.sh).  

docs/_sources/api.rst.txt

@@ -0,0 +1,34 @@
+API Reference
+=============
+
+nmslib.init
+-----------
+
+This function acts act the main entry point into NMS lib. This 
+function should be called first before calling any other method.
+
+.. autofunction:: nmslib.init
+
+nmslib.FloatIndex
+-----------------
+
+nmslib.dist.FloatIndex
+
+.. autoclass:: nmslib.dist.FloatIndex
+   :members:
+
+nmslib.DoubleIndex
+------------------
+
+nmslib.dist.DoubleIndex
+
+.. autoclass:: nmslib.dist.DoubleIndex
+   :members:
+
+nmslib.IntIndex
+---------------
+
+nmslib.dist.IntIndex
+
+.. autoclass:: nmslib.dist.IntIndex
+   :members:

docs/_sources/index.rst.txt

@@ -0,0 +1,24 @@
+.. nmslib documentation master file, created by
+   sphinx-quickstart on Mon Aug  7 17:30:15 2017.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+Non-Metric Space Library (NMSLIB)
+==================================
+
+Contents:
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents:
+
+    Quickstart <quickstart>
+    API Reference <api>
+
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`

docs/_sources/quickstart.rst.txt

@@ -0,0 +1,39 @@
+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``
+
+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.
+
+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)
+