|Author:||Lars Yencken <firstname.lastname@example.org>|
|Date:||12th Feb 2011|
SimSearch is a dictionary search-by-similarity interface for Japanese kanji, providing a nice front-end for Kanjidic. If you're viewing this source code, you should be a developer, or someone at least a little comfortable with Python.
This is a quick guide to getting SimSearch up and running locally.
SimSearch uses MongoDB as its database backend. If you don't already have it, install MongoDB first.
Next, you need Python (2.6/2.7), pip and virtualenv. Then you can install the necessary packages in an environment for simsearch:
$ pip -E ss-env install $(cat requirements.txt)
Occasionally a dependency will fail to install cleanly (e.g. NLTK). In that case, you will need to download a package for it, enter the virtual environment and install the package from there:
$ tar xfz nltk-v2.08b.tgz $ cd nltk-v2.08b $ source /path/to/simsearch/ss-env/bin/activate (ss-env) $ python setup.py install
Once you have all the packages, you are good to build! Source the environment variables needed, and get to work.
Firstly, we build the Cython module using Scons:
Secondly, we set the database building:
$ source environ_vars.sh (ss-env) $ ./simsearch/manage.py build Building similarity matrix Building neighbourhood graph
Now you're done, and can run it locally:
$ source environ_vars.sh (ss-env) $ ./simsearch/manage.py runserver
The server will now be available at http://127.0.0.1:8000/.
Please see standard Django deployment docs for installing this on a web server, e.g. using Apache and mod_python.