Building Hybrid Semantic Search in ASP.NET Core — SQL Vector, Azure AI Search, and the Bugs Between Them

May 19, 2026 · 4:31 AM UTC ·14 min read · 0 reactions · 0 comments · 31 views

#ai #azure #dotnet #semantic search #sql

TL;DR · WeSearch summary

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.

Key facts

▪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.

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DEV.to (Top) files mainly under programming. We currently carry 4,924 of its stories.

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Record

Original publisher	DEV.to (Top)
Canonical URL	https://dev.to/sharad_kumar_45b990921489/building-hybrid-semantic-search-in-aspnet-core-sql-vector-azure-ai-search-and-the-bugs-between-aed
Publication time	Tue, 19 May 2026 04:31:22 +0000
Retrieval time	2026-05-19T04:34:57.306Z
Last seen	2026-05-19T04:34:57.306Z
Headline source	Publisher (no WeSearch rewrite)
Excerpt source	publisher body
Excerpt method	First ~120 words (~800 chars) of extracted publisher body, fair-use limited.
Summary	WeSearch · cerebras-chat (WeSearch summarizer)
Summary source text	contentText
Citation coverage	Summary is a WeSearch-generated derivative; primary citation is the original publisher URL.
Cluster	CpRg1FRhfjo4
Cluster logic	Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison.
Ranking reason	Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking.
Publisher visit	Yes — open original
Substitutes article?	No — link-out required for full text

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WeSearch declared handling (basis: Derived from the published RSS/Atom feed). This is WeSearch policy, not a legal grant on the publisher's behalf.

Unknown

Retrieval and training permissions are not asserted unless the publisher confirms them.

WeSearch handling by dimension

Indexing	May the item be indexed (stored, ranked, made findable)?	Allowed
Snippet	May a short excerpt of the publisher's text be shown?	Allowed
AI summary	May WeSearch generate its own short summary of the article?	Limited
Retrieval / RAG	May the content be exposed for third-party retrieval-augmented generation?	Not asserted
Model training	May the content be used to train AI models?	Not asserted
Commercial reuse	May the content be reused commercially?	Not permitted

Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.

Open original publisher → Source rights JSON → Machine-readable provenance → Rights framework → WESEARCH-STORY-PROVENANCE-V1

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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Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).

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