Next-Gen Database Queries: AI-Driven Natural Language Interface for MongoDB

This paper present an novel concept to query MongoDB database using NLP and Large Language Model (LLM). It is called LLM Based MongoDB Querying System through which users can search in MongoDB databases just by using simple words of English language rather than using complex language queries. To help improve the ai quality we use OpenAI’s LLM to parse the user queries and create correct MongoDB queries themselves. In a large-scale experiment, system is able to classify with an average of 95 percentage accuracy. The findings of this research are important forthe database querying research and might be beneficial for multiple domains. This research is valuable, because our system can help ordinary users become more fluent in database operations and make working with databases more efficient.

Authors

Publication Details

Journal
International Journal for Research in Applied Science and Engineering Technology
Published
2024-09-09
DOI
https://doi.org/10.22214/ijraset.2024.64147
Primary Topic
Data Mining Algorithms and Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Next-Gen Database Queries: AI-Driven Natural Language Interface for MongoDB

Arpan Shaileshbhai Korat
International Journal for Research in Applied Science and Engineering Technology
Data Mining Algorithms and Applications
article

Next-Gen Database Queries: AI-Driven Natural Language Interface for MongoDB

Arpan Shaileshbhai Korat
article en

Abstract

This paper present an novel concept to query MongoDB database using NLP and Large Language Model (LLM). It is called LLM Based MongoDB Querying System through which users can search in MongoDB databases just by using simple words of English language rather than using complex language queries. To help improve the ai quality we use OpenAI’s LLM to parse the user queries and create correct MongoDB queries themselves. In a large-scale experiment, system is able to classify with an average of 95 percentage accuracy. The findings of this research are important forthe database querying research and might be beneficial for multiple domains. This research is valuable, because our system can help ordinary users become more fluent in database operations and make working with databases more efficient.

International Journal for Research in Applied Science and Engineering TechnologyVol. 12(9)
Quality Education
Openalex Percentile: Top 77%
Data Mining Algorithms and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.