What is Semantic Search?
Semantic search is a retrieval technique that finds results based on the meaning and intent of queries rather than relying purely on exact keyword matching. Instead of looking for documents containing specific query terms, semantic search uses embeddings to represent both queries and documents as vectors in a high-dimensional space, then finds documents whose vector representations are closest to the query vector. This enables finding relevant content even when it uses different terminology than the query.
The approach leverages neural embedding models that encode text into dense vectors where semantically similar content has similar vector representations. When a user issues a query, it's encoded into the same embedding space, and vector similarity (typically cosine similarity) identifies the most semantically related documents. This handles synonyms, paraphrases, and conceptual matches naturally, overcoming the vocabulary mismatch problem that plagues keyword search.
Semantic search has become foundational in modern AI applications, particularly in RAG systems and agent memory where understanding meaning is crucial. It enables more natural query formulation (users can ask questions conversationally rather than crafting keyword searches), better handling of complex information needs, and retrieval based on conceptual relevance rather than surface-level term matching. While semantic search excels at capturing meaning, it's often combined with keyword search in hybrid approaches to also benefit from exact matching capabilities.