pchunduri6/rag-demystified
📦 Open Source Projectpchunduri6
A comprehensive, from-scratch implementation of an advanced Retrieval-Augmented Generation (RAG) pipeline for LLMs.
rag-demystified is a technical repository designed to demystify the complexities of Retrieval-Augmented Generation (RAG). Unlike high-level abstractions, this project builds the pipeline from the ground up, offering clear insights into the integration of Large Language Models with external data sources. It covers the end-to-end lifecycle of a RAG system, including document ingestion, chunking strategies, embedding generation, and vector database management. The implementation focuses on modularity and clarity, allowing developers to experiment with different retrieval techniques and prompt engineering strategies. By providing a transparent look at how context is retrieved and synthesized, it helps bridge the gap between theoretical knowledge and production-ready AI applications. The codebase is highly readable, making it an excellent reference for engineers aiming to optimize their own RAG architectures for better accuracy and reduced hallucinations.
💡Highlights
- ├─Advanced RAG pipeline from scratch
- ├─Vector database integration
- └─Modular Python architecture
🎯For
- ├─AI Engineers
- ├─Backend Developers
- └─Data Scientists