Elastic AI: The next generation of search

Elastic AI: The next generation of search

Elasticsearch is known for being a powerful search and analysis engine, although Elastic Stack is multipurpose, we can use it for different use cases, highlighting its 3 large blocks, Observability, Security and searches.

While observability and security have been growing exponentially making Elastic popular, search has seen slower growth over time but this has changed with the artificial intelligence revolution.

Originally searches relied primarily on keyword matches to display results, with the advancement of AI, we now have a more sophisticated approach such as using vectors and machine learning language models.

Furthermore, Elastic has a great foundation on these topics, since over time it has evolved by adding Machine learning and perfecting its integrations with libraries.

Thanks to vectors in searches, Elastic overcomes the limitations of simple keyword matches, now offering a deeper understanding of meaning and taking advantage of the context of search terms. Vectors allow us to capture semantic nuances and contextual relationships.

To all this we can add the capacity of LLMs to process and understand natural language efficiently, thanks to these advances Elastic has marked a significant advance in search.

In future articles we will talk about ELSER and how we use it to perform semantic searches.