The industrial retrieval-augmented generation (RAG) method follows these steps: (1) construct a comprehensive industrial knowledge base from journal articles, theses, books, and patents; (2) train a BERT-based text classification model to classify incoming queries; (3) employ the GTE-DPR (General Text Embedding-Dense Passage Retrieval) model to perform word embedding and vector similarity retrieval to align query vectors with knowledge base entries; (4) refine the initial retrieved results using large language models to produce final answers.

Authors

• Person: Ronghui Liu, Hao Ren, Haojie Ren, Wu Rui, Wei Cui, Xiaojun Liang, Chunhua Yang, Weihua Gui Organization: Not available
  Knowledge Enhanced Industrial Question-Answering Using Large ...

Sources

• Knowledge Enhanced Industrial Question-Answering Using Large ... www.engineering.org.cn Ronghui Liu, Hao Ren, Haojie Ren, Wu Rui, Wei Cui, Xiaojun Liang, Chunhua Yang, Weihua Gui via serper

Referenced by nodes (2)

• Large Language Models concept
• RAG concept