Imagine a scenario where we could communicate with a relational query processor through messaging platforms like
WeChat
or
WhatsApp
, asking questions related to query processing and optimization. If realized, such a tool could prove invaluable in database education and administration, among others. In this demonstration, we present ChatQPT, a novel system that enables chat-based interactions with a relational query engine (PostgreSQL). It
combines
an LLM-powered interface with a set of specialized external tools tailored to understand various aspects of relational query processing, effectively addressing the shortcomings of both large language models and off-the-shelf RDBMSs in supporting accurate and effective conversations. Our preliminary evaluation through a user study demonstrates the promising capabilities of ChatQPT.
Hui Li, S. Bhowmick, Bao-Chao Xu et al.· Proceedings of the VLDB Endo...· 0 citations
FROG is a GPU-oriented RFANNS index that replaces multiple locally optimal substructure building with a globally aware, vertex-centric design and organizes diverse expansion neighbor candidates for each vertex in a GPU-friendly structure and rapidly identifies the expansion neighbors used for computation at query time.
Xiao-Kun Cui, Peng Liu, Jia-Dong Xie et al.· 0 citations
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