Building a Simple RAG Document Assistant with LangChain and GPT

This article demonstrates how to build a RAG-based document assistant using Python, LangChain, OpenAI GPT, and Chroma vector database. The system loads PDFs, splits them into chunks, converts them to embeddings, stores them in a vector store, and retrieves relevant context to generate answers via an LLM. RAG improves accuracy, relevance, and scalability by retrieving only pertinent document content rather than sending entire documents to the model.