End-to-End Space-Efficient Pipeline for Natural Language Query based Spacecraft Health Data Analytics using Large Language Model (LLM)

Abstract

There is a requirement of automated Space-craft Health monitoring and mission maintenance System which is able to process Natural-Language Query and revert back in required format for which size of space database is a hurdle. Hence, we propose an end-to-end customizable real-time pipeline for space mission health monitoring, utilizing LLM that addresses issue of very large databases by extracting only relevant columns in initial stages of pipeline itself leveraginf BERT for NER, LLM for fetching schema and PandasAI to execute these queries on large datasets efficiently, producing user-friendly outputs. The pipeline is robust, space-efficient, and customizable, offering features such as cross-table referencing and handling same feature names in multiple tables. We achieved 70% realtime accuracy. © 2024 IEEE.