Amazon S3 Vectors is now generally available with increased scale and production-grade performance capabilities. It offers native support to store and query vector data, potentially reducing costs by up to 90% compared to specialized vector databases.
This article explains the internal workings of vector databases, highlighting that they don't perform a brute-force search as commonly described. It details algorithms like HNSW, IVF, and PQ, the tradeoffs between recall, speed, and memory, and how different RAG patterns impact vector database usage. It also discusses production challenges like filtering, updates, and sharding.
Google DeepMind research reveals a fundamental architectural limitation in Retrieval-Augmented Generation (RAG) systems related to fixed-size embeddings. The research demonstrates that retrieval performance degrades as database size increases, with theoretical limits based on embedding dimensionality. They introduce the LIMIT benchmark to empirically test these limitations and suggest alternatives like cross-encoders, multi-vector models, and sparse models.
pgai brings AI workflows to your PostgreSQL database. It simplifies the process of building search and Retrieval Augmented Generation (RAG) AI applications with PostgreSQL by bringing embedding and generation AI models closer to the database.
