The rapid progress in artificial intelligence (AI) and big data technologies has rendered traditional databases insufficient to meet the demands of certain complex application scenarios, particularly in dealing with unstructured data such as images, speech, and text. The conventional exact matching method has proven to be inadequate in these areas. Born out of this necessity, vector databases have gained popularity in recent years. The core advantages of vector databases lie in their ability to convert data into high-dimensional vectors, allowing for more sophisticated and efficient data processing and retrieval.
Vector databases have been designed to tackle the limitations of traditional databases in processing and analyzing large volumes of complex data. By leveraging powerful machine learning algorithms and scalable architectures, vector databases can efficiently store, manage, and query complex data types, such as images, audio, and text. This has opened up new avenues for applications in areas like recommendation systems, natural language processing, computer vision, and more.
In essence, vector databases offer a promising solution for addressing the intricate challenges posed by the exponential growth of complex data. By embracing this technology, organizations can unlock new insights, improve decision-making, and enhance their competitiveness in the market.