LangChain, known for orchestrating interactions with large language models (LLMs), has introduced self-querying capabilities. This tutorial demonstrates how to perform self-querying on Milvus, the world's most popular vector database. The process involves setting up LangChain and Milvus, obtaining necessary data, informing the model about expected data format, and finally, performing self-querying. Self-querying allows an LLM to query itself using the underlying vector store, creating a simple retrieval augmented generation (RAG) app in the CVP framework.