Development of local knowledge base application using retrieval augmented generation technology - ZHU Junyi; ZHU Shangming (Tongxin xuebao)
Retrieval Augmented Generation (RAG) technology can enable large language models to access external knowledge bases by introducing external documents, thereby large language models can generate more authentic and reliable answers, and effectively solve the problems of outdated data and insufficient corpus. On the basis of introducing the basic architecture and fine-tuning techniques of large language models, the application framework of using retrieval enhanced generation technology to build a local knowledge base system was discussed. The application framework consisted of six parts: loading local documents, splitting documents, embedding splitting fragments, matching text based on questions, constructing prompts, and generating responses. Finally, based on the ERNIE-4.0 model and the AppBuilder development platform, an intelligent question answering system for campus information services was designed and developed, and a specific implementation was provided.