fastcat

Fastcat is a little Python library for quickly looking up broader/narrower relations in Wikipedia categories locally. The idea is that fastcat can be useful in situations where you need to rapidly lookup category relations, but don't want to hammer on the Wikipedia API. Fastcat relies on Redis and the SKOS file that DBpedia makes available basing on the Wikipedia MySQL dumps.

Attribution

This software is a fork of fastcat tool created by Ed Summers. Some changes were made under the Creative Commons Attribution-ShareAlike 3.0 license, and they are described in commit messages. Major changes are porting the code to Python 3 as well as adding support for more than one language.

Usage

Basic usage

The first time you import fastcat you'll need to populate your Redis database with the category data from DBpedia. To do that instantiate a FastCat object and call the load method. After that you can use it to do lookups.

>>> import fastcat
>>> f = fastcat.FastCat()
>>> f.load()  # brew a pot of coffee while the data is downloaded and loaded into redis
...
>>> print(f.broader("Computer programming"))
['Software engineering', 'Computing']
>>> print(f.narrower("Computer programming"))
['Programming idioms', 'Programming languages', 'Concurrent computing', 'Source code', 'Refactoring', 'Data structures', 'Programming games', 'Computer programmers', 'Version control', 'Anti-patterns', 'Programming constructs', 'Algorithms', 'Web Services tools', 'Programming paradigms', 'Software optimization', 'Debugging', 'Computer programming tools', 'Computer libraries', 'Programming contests', 'Archive networks', 'Self-hosting software', 'Educational abstract machines', 'Software design patterns', 'Computer arithmetic']

Non-english categories

Just fill-in the language argument in the FastCat() constructor with a language code listed below.

>>> import fastcat
>>> f = fastcat.FastCat(language='de')
>>> f.load()  # brew a pot of coffee while the data is downloaded and loaded into redis
...
>>> print(f.broader("Berlin"))
['Europa nach Ort', 'Deutschland nach Gemeinde', 'Deutschland nach Bundesland']
>>> print(f.narrower("Berlin"))
['Umwelt- und Naturschutz (Berlin)', 'Veranstaltung (Berlin)', 'Stadtplanung (Berlin)', 'Verwaltung (Berlin)', 'Urbaner Freiraum in Berlin als Thema']

Currently supported languages (and their codes)

1. English (en)
2. Estonian (et)
3. German (de)
4. Japanese (ja)
5. Polish (pl)
6. Portuguese (pt)
7. Russian (ru)
8. Ukrainian (ua)
9. Czech (cs)

Install

Redis installation

You first need to setup Redis server on your machine as follows.

On Mac:

$ brew install redis

On Linux:

$ sudo apt-get install redis-server

On Windows:

Please refer to instruction on installing Vagrant Redis. You will need an Ubuntu installation on your Windows, more information can be found here: Install your Linux Distribution of Choice

With Docker (any platform):

If you would rather not install Redis at all, the bundled compose file spins one up on localhost:6379, with the loaded categories kept in a named volume so they survive a restart:

$ docker compose up -d redis

The same file also defines a fastcat container with the package and its dev dependencies installed, which is handy for running the suite in a clean environment:

$ docker compose run --rm fastcat pytest

Inside a container Redis is not on localhost, so fastcat reads the FASTCAT_REDIS_HOST and FASTCAT_REDIS_PORT environment variables (already set for the fastcat service) to find it.

Installing the module

If you are ready, installing Fastcat is pretty straightforward:

$ pip install fastcat

Or if you wish to get the newest dev code:

$ pip install git+https://github.com/oskar-j/fastcat.git

That's it!

Contributing to the project

Guidelines

See CONTRIBUTING.md for more details

Running unit tests

With uv, which installs the exact versions from the committed uv.lock:

$ uv sync
$ uv run pytest

Or with pip:

$ pip install -e '.[dev]'
$ pytest

That runs the fast, offline tests. The end-to-end tests need a Redis server and download a SKOS dump per language from DBpedia, so they are opt-in:

$ docker compose up -d redis      # or your own local Redis
$ pytest --run-integration

Q&A

How much is is tested?

It's still in early stage of development, please share some feedback with me (under the ticket #7).

What are biggest drawbacks of Fastcat?

DBpedia SKOS file is prone to constant change, which means that downloading Wikipedia data from web can stop working in some distant future. Moreover, due to the infrastructure of Redis, you can have a maximum number of 16 languages (1 slot for a language). Last but not least, it takes around 40 MB of your web transfer (size depends on the selected language) to download a single SKOS file.

Which Python versions are supported?

Python 3.10 and above (tested on GitHub Actions against 3.10 through 3.14). Releases up to 0.1.2 supported Python 3.5+; if you are stuck on an older interpreter, pin fastcat==0.1.2.

Which languages are supported?

There are two ways to check the list of available languages.

First, is a manual inspection of the lang.py file.

Second way is to call the get_supported_languages() method on the FastCat object.

What's coming next?

Support for the rest of european languages. Exporting n-size tree of categories to a CSV or GraphML file. Moving the downloaded dumps and the language mapping out of the package directory into a proper user cache directory.

License

Creative Commons Attribution-ShareAlike 3.0