Semantic Search

Semantic search retrieves information by comparing meaning rather than relying only on exact keyword matches. A model converts queries and documents into embeddings, and a search system finds vectors located near one another. This allows a request such as “ways to lower power use” to match content about energy efficiency even when the wording differs. Production systems often combine vector similarity with keyword search, filters, freshness, and reranking. Quality depends on the embedding model, document chunks, metadata, index updates, and evaluation queries. Similar meaning can still produce an irrelevant result, so access permissions, source quality, citations, and task-specific relevance testing remain essential when search feeds an AI assistant.