Query Multiple Documents Using LlamaIndex, LangChain, and Milvus

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.