When designing search systems, the decision to use keyword-based search, vector-based search, or a hybrid approach can significantly impact performance, relevance, and user satisfaction. Each method ...

Vector databases power RAG, semantic search, recommendations, and memory. Learn how indexing, filtering, and hybrid retrieval ...

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Amazon DynamoDB recently introduced native vector search, allowing developers to store embeddings alongside application data ...

Vector similarity search uses machine learning to translate the similarity of text, images, or audio into a vector space, making search faster, more accurate, and more scalable. Suppose you wanted to ...

What is vector search and how is it transforming the search experience? Edo Liberty, CEO of Pinecone and former head of Amazon's AI lab, explains. We’ve been talking with search industry pros and ...

SAN FRANCISCO--(BUSINESS WIRE)--Elastic (NYSE: ESTC), the Search AI Company, announced new performance and cost-efficiency breakthroughs with two significant enhancements to its vector search. Users ...

When I first wrote “Vector databases: Shiny object syndrome and the case of a missing unicorn” in March 2024, the industry was awash in hype. Vector databases were positioned as the next big thing — a ...

A vector is a set of numbers. It represents data in a format machines can understand. Think of it like turning a sentence into a point in space. Vector search is a modern technique for retrieving ...

Vector search is nothing new. Its role as a critical data management capability, however, is a recent development due to the way it enables discovering data needed to inform generative AI models.