Semantic Search Advanced

Semantic search is search that understands meaning and intent, not just keywords. It uses embeddings, dense vector representations created by machine learning models, to represent the meaning of documents and queries. When a user searches, their query is converted to an embedding and matched against document embeddings using vector similarity (cosine distance). The most similar documents are returned, even if they don't contain the exact query words. Semantic search powers modern recommendation engines, chatbot knowledge retrieval, and enterprise search systems. It's the backend for RAG (Retrieval-Augmented Generation), where search results inform AI-generated answers.