How to Build a RAG Knowledge Base in Python for Customer Support

A new Retrieval-Augmented Generation (RAG) system combines vector search and AI to provide instant, accurate answers for support teams. Using LangChain, OpenAI, and SingleStore, this solution vectorizes documents, retrieves relevant information, and generates natural responses. Benefits include dynamic answers, broader coverage, and improved accuracy. The system can cut ticket handling time in half, scale seamlessly, and keep information up-to-date. Implementation involves data ingestion, embedding storage, and a query workflow that delivers context-aware responses in milliseconds.