Exploring Business Events using Multi-source RAG
Muhammad Arslan, Saba Munawar, Christophe Cruz · Procedia Computer Science · 2024
Business events signify crucial activities within a company, indicating growth opportunities and investment prospects. They encompass various developments such as recruitment drives, market expansions, mergers, and product launches. Understanding these events is vital for businesses seeking to stay updated with market dynamics, as they provide real-time insights into a company’s trajectory. Moreover, comprehending the business events of one company can offer strategic advantages to others, facilitating informed decision-making and fostering collaboration within the business ecosystem. Extracting information about these events involves diverse structured, semi-structured, and unstructured data sources, posing challenges for traditional extraction methods. Despite the promise shown by existing openly available LLMs driven by Generative Artificial Intelligence (GenAI), they face challenges when dealing with domain-specific queries. Retrieval-Augmented Generation (RAG) addresses this challenge by seamlessly integrating multiple external data sources of varying structures. In our study, we demonstrate how RAG with LLM facilitates precise extraction of business events, ensuring adaptability in dynamic business environments where datasets are constantly evolving.