I’m excited to share that I’ve published a new comprehensive tutorial on the Big Data Boutique blog about building semantic search applications with Elasticsearch as a vector database.

What the Tutorial Covers

The article, titled “Semantic Search Application with Elasticsearch Vector Database”, provides a hands-on approach to building hybrid search applications that combine both keyword and semantic search capabilities.

Building on concepts from our previous introduction to vector search, this tutorial dives deep into the practical implementation details. We work directly with Elasticsearch’s low-level APIs to handle:

• Vector mapping and configuration
• Embedding generation and indexing
• Hybrid search query construction
• Performance optimization techniques

Why This Matters

As someone who’s been working extensively with search technologies and now specializing in GenAI applications, I believe that understanding how to properly implement semantic search is crucial for modern applications. The combination of traditional keyword search with semantic understanding creates powerful search experiences that can better understand user intent.

A Hands-On Approach

Rather than just covering theory, this tutorial takes you through building a real application. You’ll learn how to leverage Elasticsearch as a vector database while gaining a deeper understanding of how vector-powered applications work under the hood.

Read the Full Tutorial

Check out the complete tutorial on the Big Data Boutique blog: Semantic Search Application with Elasticsearch Vector Database

Whether you’re new to vector search or looking to deepen your understanding of Elasticsearch’s vector capabilities, this tutorial provides practical insights you can apply to your own projects.