component-id	name	brief-description	type	release-date	release-number	work-package	pilot	keywords	changelog	licence	release link	image	logo	demo	running-instance	credits	related-components	bibliography
facets-search-engine	Facets Music Search Engine	a faceted search-engine for musical documents	Application	2022-04-14	0.5	WP1	FACETS	information retrieval search engine		GNU GPLv3								n/a

Facets Music Search Engine

This repository is dedicated to the Docker image of the FACETS pilot, focusing on the development of a faceted search-engine for musical documents.

A demo version is live on the HumaNum platform, and an older version powers the search on the NEUMA platform.

Running with Docker

First, install Docker:

For linux users, see https://docs.docker.com/desktop/install/linux-install/

For Mac users, see https://docs.docker.com/desktop/install/mac-install/

For Windows users, see https://docs.docker.com/desktop/install/windows-install/

Once Docker is installed on your machine, run the following commands in terminals:

$ git clone https://github.com/polifonia-project/facets-search-engine
$ cd facets-search-engine
$ sudo docker-compose up --build

Leave the terminal open, wait a bit so that Django and ElasticSearch are up. Then go to http://0.0.0.0:8000.

Alternatively, one can use $ docker-compose up --build -d (notice the -d, for detaching), then use docker logs Facets-WEB -f or docker logs Facets-ES -f to view logs for the web app or Elasticsearch respectively.

Populating indices with sample documents

After successfully running the Docker command, you will be faced with an empty platform. You can upload your documents, or try the FACETS features with a few samples documents (e.g., Bach chorals, Couperin Nations). Run the following commands:

$ cd GettingStarted
$ sh getting-started.sh

It should be somewhat long, but in the end you will have 3 indices on your FACETS platform, with various number of documents. You will then be able to try the search and discovery features.

Import score collections of your choice

Alternatively, you could freely choose data to import by using "Load Data" GUI interface: First, create an index by entering a name that describes the content of data that you wish to import, and click "submit". Then, click the "choose file" button, and choose a score or a zip of scores to import, and select the name of index that you wish to import the file in, and file format accordingly, and click "submit".

After file imports, you may browse the score contents in "Dashboard" section, discover imported file collections in "Discovery" section, or search for patterns using the search engine in "Search" section.

Running with Django

$ git clone https://github.com/polifonia-project/facets-search-engine.git
$ cd facets-search-engine/facets
$ python -m venv venv
$ source venv/bin/activate
$ pip install -r requirements-versions.txt
$ mkdir staticfiles
$ sudo docker start facets-es # or any other ElasticSearch starting method
$ python manage.py migrate
$ python manage.py runserver --insecure