Semantic Search

Semantic Search — explained

Classic full-text search matches tokens: searching for “car” finds documents containing “car” — not “automobile” or “vehicle”. Semantic search instead embeds query and documents into the same vector space and compares their distance. Terms that appear in similar contexts during training end up close together — including synonyms, translations, and paraphrases. That makes search robust against wording differences, but costs precision on very specific terms like SKUs or proper nouns.

Example / Practical context

In a knowledge base, an employee asks “How do I cancel an order?” — and gets back documents titled “Reversing a purchase”, “Order cancellation policy”, and “Refund workflow”, even though the exact word “cancel” never appears. In the RAG stack, semantic search is the default retrieval step; in production it is usually combined with full-text into hybrid search.

Distinction from related terms

Semantic search is the method, the vector database is the storage system behind it, RAG is the application that combines semantic search with an LLM. “Neural search” is often used as a synonym. Pure BM25 search is the lexical counterpart: fast, precise on keywords, but without understanding of meaning.

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