How to build an AI-powered private document search app with RAG, ChromaDB, and memory

A step-by-step tutorial for building a private document search app using RAG, ChromaDB, and LangChain with Python. Covers loading PDF documents, chunking text, creating vector embeddings with OpenAI, querying with GPT-3.5-turbo, and adding chat history memory for multi-turn conversations. Includes code for an interactive question loop and explains how to persist embeddings to disk.