Definition
A search approach that retrieves results based on meaning rather than keyword overlap. It typically uses vector embeddings to find documents semantically similar to a query.
In Depth
Semantic Search converts queries and documents into dense vector embeddings (usually from a transformer model) and retrieves documents whose embeddings are closest to the query embedding via cosine similarity or dot product. It can find conceptually related documents that share no keywords, e.g. 'car maintenance tips' matches 'how to keep your vehicle running.' It powers most modern RAG systems, semantic enterprise search, and recommendation systems.
Example Usage
Building a semantic search over internal Confluence docs so 'how do I configure SSO?' surfaces docs about 'enabling single sign-on' even though no keywords overlap.
Business Context
Semantic Search is the foundation of modern knowledge retrieval. Keyword search fails when users don't know the exact terms documents use, and semantic search bridges that gap.
Also Known As
Tags
Quick Info
Need help implementing this in your business?
Get Started