title: "Qu’est-ce que la recherche vectorielle ? Guide complet pour les développeurs (2024)"

Vector search is a technique for finding semantically similar items by comparing their numerical representations, known as vector embeddings. This process involves using machine learning models to convert data like text or images into vectors, which are then indexed in a specialized vector database. When a user submits a query, it is also transformed into a vector, and the system retrieves the most similar items by calculating the proximity between vectors. This method goes beyond simple keyword matching to understand context and meaning, making it essential for modern AI applications like advanced search engines, recommendation systems, and Retrieval-Augmented Generation (RAG).