Semantic Search

What Is Semantic Search?

Traditional search matches keywords: search for "car" finds documents with the word "car," missing documents about "vehicles" or "automobiles" that mean the same thing. Semantic search understands meaning: it finds documents conceptually related to your query regardless of exact wording. This matches how humans understand information.

Semantic search uses embeddings—numerical representations of meaning. Documents and queries are converted to embeddings in a shared semantic space. Search finds embeddings closest to the query embedding, returning results by semantic similarity. This approach captures meaning far better than keywords.

Semantic search enables powerful capabilities: finding similar documents automatically, recommending relevant content, and building search systems that understand complex queries. Combined with reranking (using language models to refine results), semantic search is superior for most information retrieval tasks. It's essential for RAG systems and content discovery platforms.