I’m pleased to share that I’ve published a comprehensive introduction to vector search on the Big Data Boutique blog, exploring how this powerful search technology works in OpenSearch and Elasticsearch.

What the Article Covers

The article, titled “OpenSearch and Elasticsearch Vector Search: An Introduction”, provides a foundational understanding of vector search technology and its implementation in modern search engines.

The piece explores the evolution from traditional keyword-based search to semantic search powered by dense vector embeddings. I break down complex concepts into digestible explanations, covering:

• The fundamentals of vector search and how it differs from keyword search
• Keyword search under the hood - understanding BM25 and Bag of Words representation
• Embeddings and dense vectors - how AI models transform text into meaningful numerical representations
• k-NN vs ANN algorithms - the trade-offs between accuracy and performance
• Practical use cases including semantic search, recommendation engines, and multimedia search

Bridging Theory and Practice

As someone who’s spent years working with Elasticsearch and now focuses on GenAI applications, I wanted to create content that bridges the gap between traditional search and modern AI-powered approaches. The article shows how vector search addresses the limitations of keyword search - from handling synonyms to capturing semantic context.

Why This Foundation Matters

Understanding vector search fundamentals is crucial for anyone working with modern search applications. Whether you’re building recommendation systems, implementing semantic search, or working with RAG (Retrieval-Augmented Generation) applications, these concepts form the backbone of how search engines can understand meaning rather than just matching keywords.

Read the Full Article

Dive into the complete introduction on the Big Data Boutique blog: OpenSearch and Elasticsearch Vector Search: An Introduction

This article sets the foundation for more advanced topics we’ll be covering, including the hands-on tutorial for building semantic search applications that I shared in my previous post.