SGLang is a fast serving framework for large language models and vision language models. It focuses on efficient serving and controllable interaction through co-designed backend runtime and frontend language.
This blog post explores scaling ColPali for efficient document retrieval across large collections of PDFs using Vespa's phased retrieval and ranking pipeline, including the use of a hamming-based MaxSim similarity function.
