How to handle data storage and analysis in the era of big models? Is there any application practice that has been implemented?
In order to explore these issues, Alibaba Cloud, Zilliz and Doris recently held a technical salon with the theme of "Data Storage and Analysis in the Big Model Era". Alibaba Cloud Object Storage OSS has a massive amount of unstructured data , Milvus (Zilliz), as the world's most influential open source vector database project, and Doris (Flywheel Technology), as a popular data analysis project, have accumulated a wealth of best practices for unstructured data processing and analysis.
At the salon, Li Chen, Zilliz's head of operations and ecology, shared a topic titled "Vector Database: Memory for Large Models."
Catalyzed by large models, vector databases are in the ascendant. Compared with traditional databases, vector databases are oriented to high-dimensional vectors and can better handle unstructured data such as images, audio, and videos. Li Chen mainly introduced the basic principles, application scenarios and evolution direction of vector databases, as well as Zilliz's accumulation and experience in this direction.
He said that the vector database is an important supplement to the AIGC large model and a key carrier for providing accurate, reliable, highly scalable long and short-term "memory". Its application in the LLM field can be mainly divided into the following six categories: Managing private data and knowledge The library provides real-time data updates for large models, personalizes and enhances large models, provides memory for agents, saves processing results of large models, and builds more complex AI systems. certainly,