Building a Simple RAG Document Assistant with LangChain and GPT

Large Language Models are great at generating text and answering general questions. However, they struggle when we ask questions about specific documents they have never seen before . For example: What are the key insights in this PDF report? Can you summarize section 3 of this document? LLMs alone cannot reliably answer these questions because they do not have access to your private or custom data . This is where Retrieval Augmented Generation (RAG) comes in. In this article, I will walk through how I built a RAG-based document assistant using: Python LangChain OpenAI GPT Chroma Vector Database The result is a system that allows users to chat with their documents . What We Are Building We are creating a document assistant that: Loads a PDF document Breaks it into smaller chunks Converts the chunks into embeddings Stores them in a vector database Retrieves relevant chunks when a user asks a question Uses an LLM to generate an answer based on the retrieved content Instead of manually se