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Automated subject indexing and classification tool

Project description

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Annif is an automated subject indexing toolkit. It was originally created as a statistical automated indexing tool that used metadata from the discovery interface as a training corpus.

This repo contains a rewritten production version of Annif based on the prototype. It is a work in progress, but already functional for many common tasks.

Basic install

You will need Python 3.8+ to install Annif.

The recommended way is to install Annif from PyPI into a virtual environment.

python3 -m venv annif-venv
source annif-venv/bin/activate
pip install annif

You will also need NLTK data files:

python -m nltk.downloader punkt

Start up the application:


See Getting Started in the wiki for more details.

Docker install

You can use Annif as a pre-built Docker container. Please see the wiki documentation for details.

Development install

A development version of Annif can be installed by cloning the GitHub repository.

Installation and setup

Clone the repository.

Switch into the repository directory.

Create and activate a virtual environment (optional, but highly recommended):

python3 -m venv venv
. venv/bin/activate

Install dependencies (including development) and make the installation editable:

pip install .[dev]
pip install -e .

You will also need NLTK data files:

python -m nltk.downloader punkt

Start up the application:


Unit tests

Run . venv/bin/activate to enter the virtual environment and then run pytest. To have the test suite watch for changes in code and run automatically, use pytest-watch by running ptw.

Getting help

Many resources are available:

Publications / How to cite

Two articles about Annif have been published in peer-reviewed Open Access journals. The software itself is also archived on Zenodo and has a citable DOI.

Citing the software itself

See "Cite this repository" in the details of the repository.

Annif articles

Suominen, O.; Inkinen, J.; Lehtinen, M., 2022. Annif and Finto AI: Developing and Implementing Automated Subject Indexing. JLIS.It, 13(1), pp. 265–282. URL:

  title={Annif and Finto AI: Developing and Implementing Automated Subject Indexing},
  author={Suominen, Osma and Inkinen, Juho and Lehtinen, Mona},
  doi = {10.4403/},

Suominen, O., 2019. Annif: DIY automated subject indexing using multiple algorithms. LIBER Quarterly, 29(1), pp.1–25. DOI:

  title={Annif: DIY automated subject indexing using multiple algorithms},
  author={Suominen, Osma},
  journal={{LIBER} Quarterly},
  doi = {10.18352/lq.10285},
  url = {}


The code in this repository is licensed under Apache License 2.0, except for the dependencies included under annif/static/css and annif/static/js, which have their own licenses, see the file headers for details. Please note that the YAKE library is licended under GPLv3, while Annif is licensed under the Apache License 2.0. The licenses are compatible, but depending on legal interpretation, the terms of the GPLv3 (for example the requirement to publish corresponding source code when publishing an executable application) may be considered to apply to the whole of Annif+Yake if you decide to install the optional Yake dependency.

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