Lunr.py

A Python implementation of Lunr.js by Oliver Nightingale.

A bit like Solr, but much smaller and not as bright.

This Python version of Lunr.js aims to bring the simple and powerful full text search capabilities into Python guaranteeing results as close as the original implementation as possible.

• Documentation

What does this even do?

Lunr is a simple full text search solution for situations where deploying a full scale solution like Elasticsearch isn't possible, viable or you're simply prototyping. Lunr parses a set of documents and creates an inverted index for quick full text searches in the same way other more complicated solution.

The trade-off is that Lunr keeps the inverted index in memory and requires you to recreate or read the index at the start of your application.

Interoperability with Lunr.js

A core objective of Lunr.py is to provide interoperability with the JavaScript version.

An example can be found in the MkDocs documentation library. MkDocs produces a set of documents from the pages of the documentation and uses Lunr.js in the frontend to power its built-in searching engine. This set of documents is in the form of a JSON file which needs to be fetched and parsed by Lunr.js to create the inverted index at startup of your application.

While this is not a problem for most sites, depending on the size of your document set, this can take some time.

Lunr.py provides a backend solution, allowing you to parse the documents in Python of time and create a serialized Lunr.js index you can pass have the browser version read, minimizing start up time of your application.

Each version of lunr.py targets a specific version of lunr.js and produces the same results for a non-trivial corpus of documents.

Installation

pip install lunr

An optional and experimental support for other languages thanks to the Natural Language Toolkit stemmers is also available via pip install lunr[languages]. The usage of the language feature is subject to NTLK corpus licensing clauses.

Please refer to the documentation page on languages for more information.

Usage

First, you'll need a list of dicts representing the documents you want to search on. These documents must have a unique field which will serve as a reference and a series of fields you'd like to search on.

Lunr provides a convenience lunr function to quickly index this set of documents:

>>> from lunr import lunr
>>>
>>> documents = [{
...     'id': 'a',
...     'title': 'Mr. Green kills Colonel Mustard',
...     'body': 'Mr. Green killed Colonel Mustard in the study with the candlestick.',
... }, {
...     'id': 'b',
...     'title': 'Plumb waters plant',
...     'body': 'Professor Plumb has a green plant in his study',
... }]
>>> idx = lunr(
...     ref='id', fields=('title', 'body'), documents=documents
... )
>>> idx.search('kill')
[{'ref': 'a', 'score': 0.6931722372559913, 'match_data': <MatchData "kill">}]
>>> idx.search('study')
[{'ref': 'b', 'score': 0.23576799568081389, 'match_data': <MatchData "studi">}, {'ref': 'a', 'score': 0.2236629211724517, 'match_data': <MatchData "studi">}]

Please refer to the documentation for more usage examples.

Development

Clone this repo and follow these steps:

1. Create a virtualenv on the lastest supported Python version
2. Install node.js if necessary, it will be needed for acceptance tests
3. make install-dev
4. make tests-full to check the installation is working
5. To run the tests on all Python versions you'll need to install the different supported interpreters.

• Using asdf install the asdf-python plugin