|Author:||Lars Yencken <email@example.com>|
|Date:||26th Oct 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. By default, it will create and use a database called simsearch in MongoDB.
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 ./simsearch
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
Building and running
Once installed, build the database with:
$ python -m simsearch.models Building similarity matrix Building neighbourhood graph
You can then run the debug server with the command simsearch.py. The server will be available at http://localhost:5000/.
Please see Flask documentation around deployment. Feel free to email me as well, if you have any issues.