Why vector databases are needed for search
Vector databases enable semantic similarity search by storing data as numerical vectors. They allow finding similar items based on meaning, not just exact keyword matches.
Traditional databases struggle with complex similarity calculations on high-dimensional vectors. Vector databases use specialized indexing structures (like HNSW or IVF) and approximate nearest neighbor (ANN) search algorithms to perform these searches efficiently at scale, even across billions of items. They support essential features like hybrid search combining vectors and metadata filtering.
Vector search is crucial for applications like semantic text search, recommendation systems, image search, and chatbots. It powers natural language understanding by finding contextually relevant results based on conceptual similarity, significantly enhancing user experience and uncovering hidden data relationships beyond simple keyword lookups.
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