I’m excited to share my latest tutorial on the Big Data Boutique blog, where I dive deep into using OpenSearch as a vector database through a practical, hands-on approach with real recipe data.

What the Tutorial Covers

The article, titled “Recipes to Vectors: Using OpenSearch as Vector Database”, takes you through the complete journey of building a vector-powered search application. Using a Kaggle recipe dataset, we explore how to transform text into searchable vectors and implement different search strategies.

The tutorial covers the entire workflow from data to deployment:

• Dataset selection and preparation - Working with real recipe data including ingredients, instructions, and metadata
• Embedding model selection - Choosing and implementing Cohere’s embed-english-v3 model
• Index configuration - Setting up OpenSearch with proper k-NN settings and vector field mappings
• Embedding construction - Combining multiple fields into meaningful text representations
• Data ingestion - Using OpenSearch’s Bulk API to index vectors efficiently
• Search implementation - Building keyword search, semantic search, and hybrid search queries

A Practical, Code-First Approach

What makes this tutorial unique is its hands-on nature. Rather than just explaining concepts, we work with actual data and provide complete code examples available on GitHub. You’ll see how to:

• Transform recipe names, ingredients, and instructions into effective embedding text
• Configure OpenSearch indices with proper k-NN plugin settings
• Handle real-world search scenarios like finding “Asian-inspired chicken recipes with noodles and peanuts”
• Implement hybrid search to combine semantic understanding with structured filtering

Building on the Foundation

This tutorial represents the second article in our vector search implementation series. It builds on the foundational concepts we covered earlier while complementing the Elasticsearch-focused tutorial I shared previously.

The beauty of working with recipe data is that it demonstrates both structured (categories, cooking time) and unstructured (instructions, ingredients) data scenarios - making it an excellent example for understanding when to use keyword search versus semantic search.

Why This Matters for Developers

As someone who’s been working extensively with search technologies and now focuses on GenAI applications, I believe that understanding the low-level mechanics of vector databases is crucial. This tutorial gives you the foundation to build more sophisticated applications like recommendation engines, RAG systems, and AI-powered search experiences.

Read the Complete Tutorial

Dive into the full hands-on guide on the Big Data Boutique blog: Recipes to Vectors: Using OpenSearch as Vector Database

Whether you’re new to vector search or looking to understand OpenSearch’s specific implementation, this tutorial provides the practical knowledge you need to start building vector-powered applications today.