From Concepts to Production: Real-World Patterns, Query Plans, and What’s Next

In Part I, we introduced scoped vector search in MySQL using the MyVector plugin, focusing on how semantic similarity and SQL filtering work together.

In Part II, we explored schema design, embedding strategies, HNSW indexing, hybrid queries, and tuning — and closed with a promise to show real-world usage and execution behavior.

This final part completes the series.

Semantic Search with Explicit Scope

In real systems, semantic search is almost never global. Results must be filtered by tenant, user, or domain before ranking by …

I was able to get the MySQL MCP Server up and running and it was time to try it first time against a sample data set outside of its test suite.

Let’s start with Stack Overflow and the value of its data for modern LLMs and AI systems. As most of you already know, the questions in forums has significantly dropped since the launch of OpenAI, aka ChatGPT

Source : https://x.com/alojoh/status/2009366134513127462

This is probably true for traditional search engines like Google, although they’re already adopting built-in AI solutions. Other sources, such as Reddit, also provided valuable, quick, and easy information to the masses.

I wanted to spin a subset of StackExchange questions. These are loaded into a local MySQL server. I intended to put the MCP server to a …

A lightweight, secure, and extensible MCP (Model Context Protocol) server for MySQL designed to bridge the gap between relational databases and large language models (LLMs).

I’m releasing a new open-source project: mysql-mcp-server, a lightweight server that connects MySQL to AI tools via the Model Context Protocol (MCP). It’s designed to make MySQL safely accessible to language models, structured, read-only, and fully auditable.

This project started out of a practical need: as LLMs become part of everyday development workflows, there’s growing interest in using them to explore database schemas, write queries, or inspect real data. But exposing production databases directly to AI tools is a risk, especially without guardrails.

…

Subtitle: Schema design, embedding workflows, hybrid search, and performance tradeoffs explained.

Quick Recap from Part 1

In Part 1, we introduced the MyVector plugin — a native extension that brings vector embeddings and HNSW-based approximate nearest neighbor (ANN) search into MySQL. We covered how MyVector supports scoped queries (e.g., WHERE user_id = X) to ensure that semantic search remains relevant, performant, and secure in real-world multi-tenant applications.

Now in Part 2, we move from concept to implementation:

• How to store and index embeddings
• How to design embedding workflows
• How hybrid (vector + keyword) search works
• How HNSW compares to brute-force search
…

Semantic Search with SQL Simplicity and Operational Control

Introduction

Vector search is redefining how we work with unstructured and semantic data. Until recently, integrating it into traditional relational databases like MySQL required external services, extra infrastructure, or awkward workarounds. That changes with the MyVector plugin — a native vector indexing and search extension purpose-built for MySQL.

Whether you’re enhancing search for user-generated content, improving recommendation systems, or building AI-driven assistants, MyVector makes it possible to store, index, and search vector embeddings directly inside MySQL — with full support for SQL syntax, indexing, and filtering.

What Is MyVector?

The MyVector plugin adds native support …

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