Yesterday, we built the "Library" (Vector Store). Today, we’re going to build the "Librarian." A Librarian doesn't just point you to a shelf; they go get the right book, read the relevant page, and explain it to you. In LangChain, we do this by connecting our Retriever to our LLM using a Retrieval Chain . 🏗️ The 2-Step Architecture To make our AI answer questions based on our data, we need to link two distinct parts: 1. The Retrieval Step: Finding the most relevant chunks from our Vector Database. 2. The Generation Step: Feeding those chunks into the LLM as "Context" so it can craft an answer. 🛠️ Step 1: The "Stuffing" Chain First, we need a way to tell the AI: "Here is a bunch of text (the context). Use it to answer this specific question." In LangChain, this is often called the create_stuff_documents_chain because it "stuffs" all retrieved documents into the prompt.…