This tutorial demonstrates how to use Large Language Models (LLMs) like GPT in production by querying multiple documents using LlamaIndex, LangChain, and Milvus. The process involves setting up a Jupyter Notebook, building a Document Query Engine with LlamaIndex, starting the vector database, gathering documents, creating document indices in LlamaIndex, performing decomposable querying over your documents, comparing non-decomposed queries, and summarizing how to do multi-document querying using LlamaIndex. The use of decomposable queries allows for breaking down complex queries into simpler ones that can be answered by a single data source.