Vector Search Explained

Vector search, also known as semantic search, is a transformative approach to information retrieval that leverages vector embeddings—numerical representations capturing semantic meaning—to identify similar items based on context rather than exact text matches. This technique is increasingly crucial for AI-native applications where traditional search systems are insufficient. Vector search operates by converting data into vector embeddings via language models, enabling the identification of related objects through distance metrics like cosine similarity and Euclidean distance. The use of Approximate Nearest Neighbor (ANN) algorithms in vector databases allows for rapid retrieval of relevant data, even from massive datasets, by trading a small degree of accuracy for significant speed improvements. Vector search extends beyond text to include image, audio, and video data, facilitating multi-modal and multi-lingual search capabilities. It powers various applications, including recommendation systems, retrieval augmented generation in chatbots, and hybrid search systems combining semantic and keyword search. Despite its computational demands, vector search provides a more intuitive, human-like search experience, offering results based on underlying meaning across diverse data types.