AI_dev Europe 2024

Vectors numerically embed meaning, and could be considered the language of AI. And as AI takes over the world, there's been a wide world of vector stores out there, from incumbent databases with added vector support, to fresh startups. But what's the big deal? Is a whole new product category required to store vectors? How does one even go about choosing a vector store, and deciding whether I need one? The truth is, a vector database is more than just about having an ability to store vectors. Features like vector indexes, hybrid searches, retrieval augmented generation, multi-modality and multi-tenancy significantly affect how data is stored, retrieved, augmented and isolated for users. Then, features like index types, quantization, tokenization, prompting, and replication significantly affect under-the-hood behavior and performance. And there's the matter of integration with AI models that can generate vectors, or use retrieved data to produce augmented, or transformed outputs. So join us in this talk for a deep dive into the inner workings of a vector database, and the key aspects that make them different to your grandma's database.