From MySQL Search to Semantic Search | Spring Boot + Elasticsearch + Vector Embeddings | Codefarm
In the previous video we designed a real-world search architecture evolving from a simple CRUD API to a semantic search system - • Designing a AI powered Product Service (My...
In this Part-2 video, we implement the entire system step-by-step using Spring Boot.
We start with a simple MySQL-based search implementation and gradually evolve it into a scalable production-grade search system.
Topics covered in this video:
• Building the Product Service with Spring Boot
• Implementing search using MySQL queries
• Adding MySQL Full-Text Search
• Integrating Elasticsearch for distributed search
• Indexing product data into Elasticsearch
• Comparing MySQL search vs Elasticsearch search
• Implementing vector embeddings with ollama + mxbai-embed-large
• Building a semantic search layer
• Understanding functional differences between keyword search and semantic search
By the end of this video, you will clearly see how modern applications evolve their search systems from traditional SQL queries to AI-powered semantic search.
👉 Get all the videos in this series here - • AI Powered high load product service (add/...
Chapters outline
00:00 Different components in the service
12:05 Starting the code walkthrough
34:24 Demo of the features
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The content and opinions expressed on this YouTube channel are solely those of the creator. Code samples created by the creator and presented on this channel are open-sourced and available for educational purposes only, extend it reuse as you see fit to learn. Content not to be used for commercial purposes.
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