Approximate Nearest Neighbor Benchmarks - Weaviate Podcast Recap

Please check out the full podcast here: https://www.youtube.com/watch?v=kG3ji89AFyQ

This video is a commentary on the latest Weaviate Podcast with Etienne Dilocker on ANN Benchmarks. ANN search -- short for Approximate Nearest Neighbors -- describes algorithms that enable efficient distance comparison between an encoded query vector and a vector database. For example, we may have 1 billion vectors to search through -- we don't want to do a dot product distance between our query and 1 billion candidate vectors! This podcast describes Weaviate's efforts to benchmark HNSW within the Weaviate system and give users a sense of how performance varies with respect to each dataset (and their respective properties), as well as the hyperparameters of the HNSW algorithm.

I hope you find this useful, happy to answer any questions / hold any discussion! Thank you for watching!

Видео Approximate Nearest Neighbor Benchmarks - Weaviate Podcast Recap канала Connor Shorten