Vector databases have recently gained significant attention due to the emergence of large language models that produce vector embeddings for text. Existing vector databases can be broadly categorized into two types: specialized and generalized. Specialized vector databases are explicitly designed and optimized for managing vector data, while generalized ones support vector data management within a general purpose database. While specialized vector databases are interesting, there is a substantial customer base interested in generalized vector databases for various reasons, e.g., a reluctance to move data out of relational databases to reduce data silos and costs, the desire to use SQL, and the need for more sophisticated query processing of vector and non-vector data. However, generalized vector databases face two main challenges: performance and interoperability of vector search with SQL, such as combining vector search with filters, joins, or even fulltext search. In this paper, we present SingleStore-V, a full-fledged generalized vector database integrated into SingleStore, a modern distributed relational database optimized for both OLAP and OLTP workloads. SingleStore-V achieves high performance and interoperability via a suite of optimizations. Experiments on standard vector benchmarks show that SingleStore-V performs comparably to Milvus, a highly-optimized specialized vector database, and significantly outperforms pgvector, a popular generalized vector database in PostgreSQL. We believe this paper will shed light on integrating vector search into relational databases in general, as many design concepts and optimizations apply to other databases.

Vector databases have recently emerged as a hot topic in the field of databases, especially in industry. This is due to the widespread interest in Large Language Models (LLMs), where vector databases provide the relevant context for LLMs to produce more accurate responses. However, vector data is not new. It has been studied for more than two decades, leading to many efficient algorithms and indexes for vector similarity search. Thus, a natural question is: What is really new and what is next for vector databases? This panel will bring together several leading experts in vector databases to share their insights and experiences from various perspectives. The panel will also discuss the broader role of databases, beyond just vector databases, in the era of generative AI.

The characterisitics and applications of a new digital processing instrument, particularly useful for speech, underwater acoustics, and noise analyses, will be described. Special emphasis was placed in the design to permit transient capture analysis, viewing of both the time function and its spectrum and the presentation of all information, together with the status of the analyzer on a built-in oscilloscope. Small size and portability have been achieved in an instrument the size of a small overnight suitcase by employing digital processing and the combination of hardware and programmed logic.