Applications · Established · Beginner

Semantic Search

Search that matches meaning rather than exact keywords, using embeddings to compare intent.

What Semantic Search is

A semantic engine can answer 'how do I get my money back' with a document titled 'Refund policy', which keyword search would miss entirely.

How it works

Both query and documents are embedded and compared by vector similarity. Strong systems run hybrid retrieval, blending BM25 keyword scores with vector scores and applying a reranker on top.

Why it matters

It is the retrieval quality upgrade that makes internal knowledge bases genuinely usable, and the foundation of RAG.

Common uses

→Enterprise document search
→Help centre search
→E-commerce discovery
→Codebase search

Strengths

✓Handles synonyms and phrasing differences
✓Multilingual with the right model

Watch for

✓Weaker on exact codes and identifiers
✓Requires embedding infrastructure

Continue exploring

Embeddings Vector Database Reranking Knowledge Graph