Building Hybrid Semantic Search in ASP.NET Core — SQL Vector, Azure AI Search, and the Bugs Between Them
The article discusses the challenges faced while building a hybrid semantic search system using ASP.NET Core and SQL Server. It highlights the importance of architecture decisions and the impact of seed data quality on search performance. The author shares insights on the implementation process and the unexpected results of benchmarking SQL Vector against Azure AI Search.
- ▪The author built a semantic search system on top of an existing MVC application and SQL Server database.
- ▪Initial seed data quality was poor, leading to nearly identical embeddings and random cosine similarity results.
- ▪The architecture was designed to prioritize safe keyword search before risky semantic search to ensure system reliability.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3916780) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Sharad Kumar Posted on May 19 Building Hybrid Semantic Search in ASP.NET Core — SQL Vector, Azure AI Search, and the Bugs Between Them #ai #rag #azure #dotnet Part 2 of building a public AI learning series on top of an existing Bulky MVC bookstore. Code is live at readify Most semantic search tutorials start with a fresh project, a clean vector store, and a hand-picked dataset designed to make the demo look good. I had none of that.
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