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Semantic search

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Semantic search is an approach to search that matches results by meaning and intent, not just exact keyword overlap. In modern systems, this is commonly implemented by representing queries and content as embeddings (numeric vectors that capture semantic relationships), then retrieving items with the most similar vectors. That lets a query like “best wine for seafood” match content such as “good with fish,” even when exact terms differ.

In short: semantic search focuses on meaning, so it can return relevant results even when users and documents use different wording.

How semantic search works

A common implementation turns meaning into geometry, where similarity can be measured:

  1. Embed the content: Each document or passage is converted into an embedding and stored (typically ahead of time), often in a vector database.
  2. Embed the query: At search time, the query is encoded with the same model family.
  3. Compare by similarity: The system finds stored vectors closest to the query vector (for example, with cosine similarity) and computes a retrieval score.
  4. Return top matches: Higher-scoring items are returned as the most semantically relevant results.

The key idea is that semantically similar items tend to be close in representation space, so distance can act as a proxy for relevance.

  • Keyword (lexical) search matches literal terms and is strong for names, codes, and rare exact strings, but it can miss paraphrases and synonyms. Semantic search improves recall for reworded queries, though it may underweight strict literal matching.
  • Hybrid search combines lexical and semantic signals, usually fusing both result sets. Relative to hybrid search, semantic search alone refers to the meaning-focused component.
  • Vector search is a common retrieval mechanism for semantic search (nearest-neighbor lookup over embeddings). In practice the terms are often used interchangeably, but “semantic search” is the broader objective: relevance by meaning.

Where semantic search is used

  • End-user search: Consumer and site search experiences that better handle natural-language phrasing and intent.
  • RAG and AI answer engines: Semantic retrieval selects passages that best match a question’s meaning, then supplies that context to an LLM.
  • Recommendations and discovery: Finding related products, articles, or items by semantic similarity rather than shared tags alone.
  • Web search APIs: A search API may expose semantic or neural ranking so developers can build meaning-aware retrieval layers for RAG and related AI workflows without operating their own embedding and vector stack.

Semantic search is especially useful when users’ phrasing and source wording differ, which is often true in natural-language queries.

Embeddings, vector database, hybrid search, keyword search, cosine similarity, retrieval score, reranking, retrieval-augmented generation (RAG), large language model (LLM).