The author discusses the concept of “Retrieval Augmented Generation” (RAG), which enhances content generation by adding context to queries for better answers. They explore the use of binary vector space to save on server costs and streamline in-memory retrieval. By using precise vector comparisons, they aim to optimize search processes. Surprisingly, they achieve rapid results by leveraging efficient hamming distance calculations. Additionally, by implementing StaticArrays, they further enhance performance, showcasing the potential for substantial speed improvements in search operations. Ultimately, their experiments demonstrate the effectiveness of utilizing binary vector space for efficient information retrieval.
https://domluna.com/blog/tiny-binary-rag