Semantic Search

Semantic search is an information retrieval approach that finds results based on the meaning and intent of queries rather than exact keyword matches. By representing both queries and documents as vector embeddings in a shared semantic space, semantic search can identify relevant content even when the specific words differ, understanding that "automobile repair" and "car mechanic" refer to the same concept.

The semantic search pipeline begins with encoding documents and queries into dense vector representations using embedding models. These vectors capture semantic meaning such that similar concepts are positioned near each other in the embedding space. At query time, the query is encoded into the same vector space, and the nearest document vectors are retrieved using similarity metrics like cosine similarity or dot product, implemented efficiently through approximate nearest neighbor algorithms in vector databases.

Modern production search systems typically implement hybrid search, combining semantic vector search with traditional keyword-based methods like BM25. This hybrid approach captures both semantic understanding and exact term matching, which is important for queries containing specific identifiers, product codes, or technical terms that should be matched literally. Reranking models then re-score the combined results using cross-encoder architectures that consider the full query-document interaction, producing a final ranked list that outperforms either method alone.

Semantic search is a foundational component of RAG systems, enterprise knowledge bases, e-commerce product discovery, and customer support automation. Key implementation considerations include selecting and optionally fine-tuning embedding models for your domain, designing chunking strategies that preserve semantic coherence, implementing metadata filtering for access control and faceted search, handling multilingual content, and monitoring retrieval quality through metrics like recall, precision, and mean reciprocal rank.