Retrieval Augmented Generation on Notion Docs via LangChain

This tutorial demonstrates how to build a retrieval augmented generation (RAG) type app using LangChain and Milvus. The process involves reviewing LangChain self-querying, working with Notion docs in LangChain, ingesting Notion documents, storing them in a vector database, and querying the documents. The tutorial uses LangChain for operational framework and Milvus as the similarity engine. It covers how to load and parse a Notion document into sections to query in a basic RAG architecture, with future tutorials exploring different chunking strategies, embeddings, splitting strategies, and evaluation methods.