Metadata-Version: 2.1
Name: skipchunk
Version: 0.9.8
Summary: Easy natural language concept search for the masses.
Home-page: https://github.com/o19s/skipchunk
Author: Max Irwin
Author-email: mirwin@opensourceconnections.com
License: MIT license
Keywords: skipchunk
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Dist: spacy (==2.3.2)
Requires-Dist: nltk (==3.5)
Requires-Dist: pysolr (==3.9.0)
Requires-Dist: elasticsearch (==7.9.1)
Requires-Dist: Click (>=6.0)
Requires-Dist: tqdm (>=4.48)
Requires-Dist: requests (>=2.23)
Requires-Dist: jsonpickle (==1.4.1)
Requires-Dist: bs4 (==0.0.1)
Requires-Dist: lxml (==4.5.2)
Requires-Dist: multidict (==4.7.6)

# Skipchunk

[![Pypi](https://img.shields.io/pypi/v/skipchunk.svg)](https://pypi.python.org/pypi/skipchunk)

[![Travis build status](https://img.shields.io/travis/binarymax/skipchunk.svg)](https://travis-ci.org/binarymax/skipchunk)

[![Documentation Status](https://readthedocs.org/projects/skipchunk/badge/?version=latest)](https://skipchunk.readthedocs.io/en/latest/?badge=latest)

Easy natural language concept search for the masses.

Out of the box it provides a hassle-free autosuggest for any corpus from scratch, and latent knowledge graph extraction and exploration.

* Free software: MIT License
* Documentation: https://skipchunk.readthedocs.io.

## Install

```bash
pip install skipchunk
python -m spacy download 'en_core_web_lg'
python -m nltk.downloader wordnet
```

You also need to have Solr or Elasticsearch installed and running somewhere!  The current supported version is 8.4.1, but it might work on other versions.

## Use It!

See the ```./example/``` folder for an end-to-end OSC blog load and query


## Features

* Identifies all the noun phrases and verb phrases in a corpus
* Indexes these phrases in Solr for a really good out-of-the-box autosuggest
* Structures the phrases as a graph so that concept-relationship-concept can be easily found
* Keeps enriched content ready for reindexing

## Credits

Developed by Max Irwin, OpenSourceConnections https://opensourceconnections.com

All the blog posts contained in the example directory are copyright OpenSource Connections, and may not be redistributed without permission

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History
=======

0.1.0 (2019-06-18)
------------------

* Cookie-cutted

0.9.0 (2020-09-25)
------------------

* First release on PyPI.


