Fundamentals

Semantic Search

Semantic search finds relevant documents based on the meaning of your query, not just keyword matching. You convert both the query and documents to embeddings, then find the closest embeddings. This works for synonyms, paraphrases, and conceptually related content that keyword search would miss. Semantic search powers modern RAG systems, recommendation engines, and question-answering systems. Unlike traditional keyword search, it understands that 'car' and 'automobile' are semantically similar. Building semantic search requires an embedding model and a vector database.