Exploring Apache Lucene with Python: Understanding Search Engines

Have you ever wondered how search engines can find information in a bunch of text almost instantly? Behind the "magic", there are structures and algorithms that index and retrieve this information. One of the most popular tools for this is Apache Lucene.

And who is Apache Lucene?
Lucene is an open-source library written in Java, used for indexing and searching text and its implementation is the basis for other projects and platforms, such as ElasticSearch and Solr.

And to illustrate the concepts of Lucene I decided to implement a simplified version in Python.

How does the search technique work?
The search technique used follows the following steps:

• Query Preprocessing:

The query is subjected to the same process of tokenization, normalization, removal of stop words and stemming that documents went through during indexing.

• Inverted Index Search:

For each term processed in the query, we retrieve the documents where the term appears, along with the TF-IDF weight calculated during indexing.

• Document Combination and Punctuation:

Term scores are summed for each document, reflecting the relevance of the document to all terms in the query.

• Ordering of Results:

Documents are sorted descending based on total score, ensuring the most relevant results are presented first.

Result

Repository link on GitHub?
https://github.com/joaodest/Artigos/lucene.py

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