Building an Intelligent QA System with NLP and Milvus

This post details the architecture and implementation of an intelligent QA system using BERT for semantic understanding and Milvus for vector search. It covers data preparation from the insurance industry corpus, generating feature vectors using a pre-trained BERT model, importing these vectors into Milvus with associated IDs, and storing the question IDs and their corresponding answers in PostgreSQL. The retrieval process involves converting user questions to feature vectors, performing similarity search in Milvus using cosine distance, and fetching the answer from PostgreSQL based on the retrieved ID. A similarity threshold is suggested for handling cases where no closely matching question is found.