Efficient Large Language Model Application Development: A Case Study of Knowledge Base, API, and Deep Web Search Integration
Xiangyu Wang, Yan Ling Tan, Tao Yang, Meng Jun Yuan, Shaohan Wang, Min Chen, Feiyang Ren, Zijian Zhang, Yuqi Shao · Journal of Computer and Communications · 2024
This paper presents a reference methodology for process orchestration that accelerates the development of Large Language Model (LLM) applications by integrating knowledge bases, API access, and deep web retrieval. By incorporating structured knowledge, the methodology enhances LLMs’ reasoning abilities, enabling more accurate and efficient handling of complex tasks. Integration with open APIs allows LLMs to access external services and real-time data, expanding their functionality and application range. Through real-world case studies, we demonstrate that this approach significantly improves the efficiency and adaptability of LLM-based applications, especially for time-sensitive tasks. Our methodology provides practical guidelines for developers to rapidly create robust and adaptable LLM applications capable of navigating dynamic information environments and performing effectively across diverse tasks.