The vector database market is experiencing rapid growth, with projections estimating it will reach $10.6 billion by 2032, ...

These missing databases are graph databases, a NoSQL approach that provides an easy route to a vector representation of your data with the added bonus of encoding relationships in the vertices ...

Vector databases are famous for similarity searches ... ANN-Benchmark allows you to graph the results of testing recall/queries per second of various algorithms based on any of a number of ...

There’s been a debate of sorts in AI circles about which database is more important in finding truthful information in generative AI applications: graph or vector databases. AWS decided to leave ...

Nvidia also announced a new integration between its RAFT (Reusable Accelerated Functions and Tools) graph acceleration library and Milvus. Nvidia CEO Jensen Huang spoke about the importance of vector ...

Vector databases are transforming how unstructured ... Popular ANN methods include: HNSW (Hierarchical Navigable Small World): A graph-based algorithm that balances speed and accuracy, making ...

A graph database is a type of data model structured ... That’s an area where vector databases are playing a role and it’s a space that Iyer is tracking closely. “We’re looking at it ...

While it offers just about every other database type–including a graph database, which also was enhanced with vector capabilities–AWS did not announce a dedicated vector database, as some were ...

Graph database technology, working with vector search and data science, can improve GenAI applications with more accurate responses and deep explainability of analytical results, according to Neo4j.