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 …