Text Retrieval and Search Engines Technologies ( Natural Language Processing, Solr, Lucene, Elasticsearch, etc)

Define retrieval augmented generation and its use cases in technology and applications. Understand the benefits, challenges, and future trends in retrieval augmented generation

Abstract

Search has become ubiquitous but that does not mean that search has been solved. Enterprise search, which is broadly speaking the use of information retrieval technology to find information within organisations, is a good example to illustrate this. It is an area that is of huge importance for businesses, yet has attracted relatively little academic interest. This monograph will explore the main issues involved in enterprise search both from a research as well as a practical point of view. We will first plot the landscape of enterprise search and its links to related areas. This will allow us to identify key features before we survey the field in more detail. Throughout the monograph we will discuss the topic as part of the wider information retrieval research field, and we use Web search as a common reference point as this is likely the search application area that the average reader is most familiar with.

Solr vs. Elasticsearch. Elasticsearch vs. Solr.  Which one is better? How are they different? Which one should you use? Before we start, check out two useful Cheat Sheets to guide you through both …

Last week, a colleague asked me a really simple question about segments merging in Solr. After discussing the answer for some minutes while playing aroun

Amazon Search: The Joy of Ranking Products: Amazon is one of the world’s largest e-commerce sites and Amazon Search powers the majority of Amazon’s sales

The focus of this post is sentiment analysis. This is a Natural Language Processing (NLP) application I find challenging but enjoyable. It aims at identifying emotional states, reactions and subjective information. It tries to determine the attitude of a speaker with respect to some topic. If done automatically with high precision and on a large scale, sentiment analysis could be a goldmine for marketers or politicians who want to measure the public opinion through social networks. In this post I'll show you how I built a machine learning model that classifies tweets with respect to their polarity. Tweets are short and yet capture lots of subjective information. That's why we'll be playing with them. Some words for those who are ready to dive in the code: I'll be using python, gensim, the word2vec model and Keras.

Instead of going through Elasticsearch’s documentation yet another time or looking at random Stack Overflow answers, just save this in your favorites!

How data powers search and location relevance at airbnb

Globalization at Netflix Having launched the Netflix service globally in January, we now support search in 190 countries.  We currentl

We take a look at how to implement autocomplete using Elasticsearch and nGrams in this post. Read on for more information.

As Ops/DevOps, we are usually more concerned about cluster health and the data inside. This article is a simple step-by-step guide to check Elasticsearch, cluster, nodes, shards, indices, documents, and more.