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You're reading from  Vector Search for Practitioners with Elastic

Product typeBook
Published inNov 2023
PublisherPackt
ISBN-139781805121022
Edition1st Edition
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Authors (2):
Bahaaldine Azarmi
Bahaaldine Azarmi
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Bahaaldine Azarmi

Bahaaldine Azarmi, Global VP Customer Engineering at Elastic, guides companies as they leverage data architecture, distributed systems, machine learning, and generative AI. He leads the customer engineering team, focusing on cloud consumption, and is passionate about sharing knowledge to build and inspire a community skilled in AI.
Read more about Bahaaldine Azarmi

Jeff Vestal
Jeff Vestal
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Jeff Vestal

Jeff Vestal has a rich background spanning over a decade in financial trading firms and extensive experience with Elasticsearch. He offers a unique blend of operational acumen, engineering skills, and machine learning expertise. As a Principal Customer Enterprise Architect, he excels at crafting innovative solutions, leveraging Elasticsearch's advanced search capabilities, machine learning features, and generative AI integrations, adeptly guiding users to transform complex data challenges into actionable insights.
Read more about Jeff Vestal

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Search experience in Elastic before vectors

Before the introduction of vector search in Elastic, the primary relevancy model was based on text search and analysis capabilities. Elasticsearch provides various data types (https://www.elastic.co/guide/en/elasticsearch/reference/current/mapping-types.html) and analyzers (https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-analyzers.html) to provide efficient search. In this part, we will level-set and make sure we all have an understanding of what the “before state” looks like.

Data type and its impact on relevancy

There are various data types in Elasticsearch, but for the purpose of this part, it wouldn’t be useful to go through them one by one. Instead, we will divide them into two categories: the type that directly drives the relevancy ranking and the types that indirectly influence the ranking. The goal is to understand how they are related to relevancy models.

The first type that...

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Vector Search for Practitioners with Elastic
Published in: Nov 2023Publisher: PacktISBN-13: 9781805121022

Authors (2)

author image
Bahaaldine Azarmi

Bahaaldine Azarmi, Global VP Customer Engineering at Elastic, guides companies as they leverage data architecture, distributed systems, machine learning, and generative AI. He leads the customer engineering team, focusing on cloud consumption, and is passionate about sharing knowledge to build and inspire a community skilled in AI.
Read more about Bahaaldine Azarmi

author image
Jeff Vestal

Jeff Vestal has a rich background spanning over a decade in financial trading firms and extensive experience with Elasticsearch. He offers a unique blend of operational acumen, engineering skills, and machine learning expertise. As a Principal Customer Enterprise Architect, he excels at crafting innovative solutions, leveraging Elasticsearch's advanced search capabilities, machine learning features, and generative AI integrations, adeptly guiding users to transform complex data challenges into actionable insights.
Read more about Jeff Vestal