iPDB: SQL with ML and LLM Predicates (Towards a Database Engine for AI)
| Paper | iPDB: SQL with ML and LLM Predicates (Towards a Database Engine for AI) (PDF) |
| Implementation | Code |
| Venue | VLDB 2026 (demo) |
Abstract
Structured Query Language (SQL) has remained the standard query language for databases, and is highly optimized for processing structured data. However, it is inefficient for applications that leverage the capabilities of large language models (LLMs) to comprehend and extract semantic information from structured and unstructured data. This results in complex engineering and multiple data migration operations that transfer data between the data source and the LLM inference platform to couple them. We demonstrate iPDB, a system that supports in-database LLM inference using an extended declarative SQL syntax and new optimizations that result in efficient query processing of LLM-enabled SQL queries that outperform state-of-the-art systems.
