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 ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Vivek Yadav, an engineering manager from ...

MongoDB enables millions of developers to securely build AI applications on any infrastructure, from local machines to on-premises data centers NEW YORK, Sept. 17, 2025 /PRNewswire/ -- MongoDB, Inc.

The emergence of vector databases and vector search for handling massive quantities of complex data have radically transformed the way AI is implemented and managed. As a specialized approach for ...

Cloud database provider MongoDB Inc. today announced a slew of updates to its platform that are designed to help software developers build applications that can harness the power of generative ...

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 ...

Most vector search systems struggle with a basic problem: how to break complex documents into searchable pieces. The typical approach is to split text into fixed size chunks of 200 to 500 tokens, this ...

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 ...

Despite the aggressive cost claims and dramatic scale improvements, AWS is positioning S3 Vectors as a complementary storage ...