Integrating blockchain with LLM for real-time insight into transactions in an e-auction system

Real-time data retrieval and analytics play a crucial role in digital systems, requiring efficient, secure, and automated mechanisms for extracting insights from structured and unstructured data. Traditional methods rely on centralized databases and SQL (Structure Query Language) queries, which may lack security, transparency, adaptability and automation. Blockchain (BC) technology introduces a decentralized and tamper-proof ledger that enhances data integrity, while large language models (LLMs) leverage natural language processing (NLP) and excel in contextual understanding to facilitate more intuitive and flexible querying mechanisms. Implementing these technologies brings promising solutions, but some challenges remain, particularly in retrieving contextual information and data analysis. Hence, this research paper explores the integration of BC with LLM in an e-auction system using three different integration alternatives: the Pandas Query Engine (PQE), Retrieval-Augmented Generation (RAG) and Custom Query Pipeline (CQP). We compare the accuracy, and the time elapsed on querying of these methods in extracting relevant information from online auction event logs. Among three integration alternatives, RAG provides better result in terms of accuracy and PQE provides quicker result in terms of response time. Our results provide guidance on selecting retrieval strategies for AI-driven blockchain analytics, with implications for bid monitoring, regulatory compliance, fraud detection and recommendations to users of e-auction systems about efficient bidding. Further, we have discussed about cost of using these techniques.