LLM-powered GraphQL Generator for Data Retrieval
Balaji Ganesan, Sambit Ghosh, Nitin Gupta, Manish Kesarwani, Sameep Mehta, Renuka Sindhgatta
Abstract
GraphQL offers an efficient, powerful, and flexible alternative to REST APIs. However, application developers writing GraphQL clients need both technical and domain-specific expertise to reap its benefits, and avoid over-fetching or under-fetching data. Automated GraphQL generation has so far proven to be a hard problem because of complex GraphQL schema and lack of benchmark datasets. To address these issues, our work focuses on building an LLM-powered pipeline that can accept user requirements in natural language along with the complex GraphQL schema and automatically produce the GraphQL query needed to retrieve the necessary data. Automated GraphQL generation helps reduce entry barriers to application developers, broadening GraphQL adoption.
BibTeX
@inproceedings{ijcai2024p1002,
title = {LLM-powered GraphQL Generator for Data Retrieval},
author = {Ganesan, Balaji and Ghosh, Sambit and Gupta, Nitin and Kesarwani, Manish and Mehta, Sameep and Sindhgatta, Renuka},
booktitle = {Proceedings of the Thirty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-24}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Kate Larson},
pages = {8657--8660},
year = {2024},
month = {8},
note = {Demo Track},
doi = {10.24963/ijcai.2024/1002},
url = {https://doi.org/10.24963/ijcai.2024/1002},
}