Local RAG Magic: Mastering Research Papers with a Budget GPU

This project showcases an impressive feat of running a full Retrieval-Augmented Generation (RAG) pipeline locally, demonstrating how to process research papers without relying on external APIs. By combining the BGE-M3 embedding model, the Qwen2.5-32B Large Language Model (LLM), and ChromaDB, the author provides a practical guide for researchers on resource-constrained hardware. This is an exciting step toward democratizing access to advanced AI tools!