A LLM+OLAP Solution

In this web content, the author discusses their experience using the Apache Doris OLAP engine and how they have optimized it with the use of Large Language Models (LLM). The LLM is used to transform natural language questions into SQL statements, but it has limitations in understanding data jargon and niche knowledge. To overcome these challenges, the author introduces a semantic layer, LLM parsing rules, a Schema Mapper, and external knowledge bases. They also describe the SuperSonic framework, which streamlines the query process. The author also shares their architectural optimization experience, including streamlining links and splitting flat tables. They mention other useful functionalities of Apache Doris, such as Materialized Views and the Flink-Doris-Connector. The author concludes by discussing their future plans to test new features of Doris and seek input on their SuperSonic framework. Overall, the content provides detailed insights into the author’s experiences and strategies with using Apache Doris for data management.


To top