Semantic Search
Semantic search is a retrieval method that matches queries to documents based on conceptual meaning rather than keyword overlap, using embedding vectors and similarity metrics to surface contextually relevant results regardless of exact wording.
Traditional lexical search (BM25, TF-IDF) scores documents by how many query terms they contain and how rare those terms are — it is fundamentally a word-counting exercise. Semantic search replaces this with a geometric operation in embedding space: both the query and every candidate document are encoded into dense vectors, and retrieval is performed by finding the vectors closest to the query vector. Because the embedding model has learned that "car" and "automobile" are semantically related, documents about automobiles rank highly for a query about cars even if the word "car" never appears in those documents.
A semantic search system operates in two phases. Offline, a corpus of documents is encoded by an embedding model and the resulting vectors are stored in a vector database with an ANN index. Online, the user's query is encoded by the same model and the index retrieves the top-K most similar document vectors, ranked by cosine similarity or dot product. In production, semantic (dense) retrieval is commonly combined with lexical (sparse) retrieval in a hybrid architecture: BM25 retrieves keyword matches, an ANN index retrieves semantic matches, and the combined results are re-ranked by a cross-encoder model that scores each candidate by reading the query and document jointly.
Semantic search addresses the vocabulary mismatch problem that plagues lexical systems: users rarely phrase queries using the same words authors use in documents. It improves recall for paraphrases, synonyms, and conceptual queries, and it enables multilingual retrieval since multilingual embedding models place equivalent phrases in different languages close together in vector space. For enterprise knowledge bases, product catalogs, and scientific literature, semantic search surfaces relevant content that keyword systems would miss entirely.
As of 2026, semantic search is a standard component of enterprise search platforms including Microsoft SharePoint Copilot, Elastic with ELSER, and Algolia NeuralSearch, as well as the retrieval layer of most RAG pipelines. The dominant production approach is hybrid search with re-ranking: dense retrieval for recall, sparse retrieval for precision, and a cross-encoder for final ordering. A remaining challenge is handling very long documents: naive full-document embedding loses fine-grained information, driving adoption of chunking strategies and late-interaction models such as ColBERT that compare query and document at the token level.