Optimal Graph Model Schema Injection for Large Language Model-Generated Cypher Queries

Abstract

Platform ecosystems have transformed the way value is created in different industries. The data traces of such ecosystems are typically represented through graph models and databases. Retrieval of relevant data from such databases requires writing extensively complex queries to travers such complex networks to fetch and slice the correct sub-graphs corresponding to the original business inquiry. Advances in generative artificial intelligence, namely large language models (LLMs), can provide a no-code interface to such complex databases by generating and executing database queries that fetch the correct and relevant data in response to user prompts and inquiries. However, for the LLM to generate the right query, data about the schema of the database and the underlying graph model must be provided. In this study, we present a pipeline for evaluating different techniques for injecting the database schema in the LLM prompts, in addition to preliminary evaluation results.