Metadata-Version: 2.4
Name: fastcat
Version: 0.2.1
Summary: Navigate Wikipedia categories quickly in a local redis instance
Author-email: Ed Summers <ehs@pobox.com>
Maintainer-email: Oskar Jarczyk <oskar.jarczyk@gmail.com>
License-Expression: CC-BY-SA-3.0
Project-URL: Homepage, https://github.com/oskar-j/fastcat
Project-URL: Repository, https://github.com/oskar-j/fastcat
Project-URL: Changelog, https://github.com/oskar-j/fastcat/blob/master/CHANGELOG.md
Project-URL: Issues, https://github.com/oskar-j/fastcat/issues
Keywords: Wikipedia,categories,wiki-api,knowledge engineering
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Requires-Dist: redis<8.0.0,>=5.0.0
Requires-Dist: pycountry<26.0.0,>=24.6.1
Provides-Extra: dev
Requires-Dist: pytest<9.0.0,>=8.0.0; extra == "dev"
Dynamic: license-file

fastcat
=======

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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](http://en.wikipedia.org/w/api.php). Fastcat relies on Redis and the 
[SKOS file](http://downloads.dbpedia.org/current/en/skos_categories_en.nt.bz2) that DBpedia makes available basing on 
the Wikipedia [MySQL dumps](http://dumps.wikimedia.org/enwiki/latest/).

![fastcat logo](https://datageek.pl/assets/img/projects/fast_cat.png)

Attribution
-----

This software is a fork of [fastcat](https://github.com/edsu/fastcat) tool created by [Ed Summers](https://github.com/edsu). 
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.

```python
>>> 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.

```python
>>> 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](https://github.com/ServiceStack/redis-windows). You will
need an Ubuntu installation on your Windows, more information can be found 
here: [Install your Linux Distribution of Choice](https://docs.microsoft.com/pl-pl/windows/wsl/install-win10)

**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](https://github.com/oskar-j/fastcat/blob/master/CONTRIBUTING.md) for more details

#### Running unit tests

Install the package with its dev dependencies and run pytest:

```
$ 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](https://github.com/oskar-j/fastcat/issues/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](http://www.mikeperham.com/2015/09/24/storing-data-with-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](https://github.com/oskar-j/fastcat/blob/master/src/fastcat/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](http://creativecommons.org/licenses/by-sa/3.0/)
