Dolphin VectorDB: A High-Performance Vector Database for AI Applications

This article introduces Dolphin VectorDB, a high-performance vector database developed by Alibaba's Alimama team. The database addresses the growing demand for vector computing and storage driven by AI generative technologies like OpenAI. Dolphin VectorDB offers advantages in scalability, performance, cost-effectiveness, and ease of development, with successful applications in content risk control, marketing knowledge Q&A, and AI audience selection.

The article discusses the current state of vector database technology, comparing specialized vector databases with databases that support vector capabilities. It outlines the evolution of Dolphin's vector engine from version 1.0 to the current VectorDB, highlighting key improvements in each iteration.

The technical architecture of Dolphin VectorDB is detailed, including five main modules: data access, query, execution, storage, and metadata management. The database supports both online and offline consistency, real-time updates, and multiple query modes for different scale requirements.

Core capabilities include in-situ consistency between online and offline scenarios, real-time update support for large model scenarios, and multi-mode query options. The article also presents business applications, particularly in content risk control, where Dolphin VectorDB has shown significant improvements in performance, cost, and development efficiency.

Future directions for Dolphin VectorDB include further enhancement of vector computing capabilities and integration of multi-modal computing abilities to address complex computing needs in AI applications.