In this demonstration, we present AnDB, an AI-native database that supports traditional OLTP workloads and innovative AI-driven tasks, enabling unified semantic analysis across structured and unstructured data. While structured data analytics is mature, challenges remain in bridging the semantic gap between user queries and unstructured data. AnDB addresses these issues by leveraging cutting-edge AI-native technologies, allowing users to perform semantic queries using intuitive SQL-like statements without requiring AI expertise. This approach eliminates the ambiguity of traditional text-to-SQL systems and provides a seamless end-to-end optimization for analyzing all data types. AnDB automates query processing by generating multiple execution plans and selecting the optimal one through its optimizer, which balances accuracy, execution time, and financial cost based on user policies and internal optimizing mechanisms. AnDB future-proofs data management infrastructure, empowering users to effectively and efficiently harness the full potential of all kinds of data without starting from scratch.

本文提出了AnDB，一个支持传统在线事务处理和创新AI驱动任务的AI原生数据库，旨在解决用户查询与非结构化数据之间的语义鸿沟。AnDB通过运用先进的AI技术，使得用户能够使用直观的类似SQL的语句进行语义查询，简化了传统文本到SQL系统的模糊性，实现了对各种数据的无缝优化分析，可以有效支持数据管理基础设施的未来发展。

AnDB：借助AI原生数据库实现通用语义分析的突破