Semantic Search Applications
Semantic search products — support search, internal knowledge search, e-commerce search — apply vector retrieval to a specific domain.
Prerequisites
Overview
The same vector-search mechanism powers very different products depending on the domain — matching a support ticket to a help article, an internal search over company docs, or matching a shopper’s query to relevant products even when the wording doesn’t match exactly.
Where It Fits
Vector Search
The core mechanismSupport Search
Internal Knowledge Search
E-Commerce Search
Key Points
- Domain-specific relevance
- What counts as a "good match" differs by domain — an e-commerce query cares about intent and category, a support query cares about resolution steps.
- Query understanding
- A short, ambiguous product search query often needs more query transformation than a well-formed support question.
- Feedback loops
- Click and purchase data can be used to tune ranking over time in a way pure semantic similarity alone doesn’t capture.
Interview Question
Why might an e-commerce search need more than plain vector similarity to work well?
A shopper’s query often expresses intent loosely, and relevance depends on business signals — category, price, popularity, past purchase behavior — that pure semantic similarity doesn’t capture. Production semantic search products typically combine vector similarity with these domain-specific ranking signals, not similarity alone.
Explain It in 30 Seconds
The same vector-search mechanism underlies very different products — support search, internal knowledge search, e-commerce search — but what counts as relevant, and what signals get combined with similarity, differs by domain.
Real-World Stack
Technologies commonly used to implement this in production.