A search technique that uses NLP to understand the intent and contextual meaning of a query, rather than matching exact keywords.
Unlike traditional lexical search engines (e.g., Elasticsearch), Semantic Search relies on Vector Embeddings to map queries and documents into a high-dimensional mathematical space. This lets the system return relevant results based on conceptual similarity. In a typical RAG pipeline, semantic search is the retrieval step.
See also Large Language Model, Fine-Tuning and Model Inference.