This repository is a comprehensive, educational collection of 42+ Jupyter notebooks and runnable scripts demonstrating a wide array of Retrieval-Augmented Generation (RAG) techniques. It is primarily a data_research and educational resource, not a deployable application or library. The codebase serves as a practical cookbook for engineers and researchers, covering the full RAG stack from basic chunking and retrieval (e.g., simple RAG, BM25) to advanced methods like GraphRAG, Self-RAG, HyDE, RAPTOR, and agentic RAG. It also includes a dedicated evaluation framework with notebooks and reusable scripts for measuring faithfulness, relevance, and correctness using tools like DeepEval and custom LLM judges. The architecture is modular, with a shared helper_functions.py providing common utilities for PDF loading, embedding, and retrieval, and an evaluation/ module offering reusable evaluation pipelines. The runnable scripts in all_rag_techniques_runnable_scripts/ provide cleaner, class-based implementations of the techniques that are more suitable for integration into other projects than the notebooks themselves. While the code is well-structured for an educational project and demonstrates high craftsmanship in its reusable components, it is not designed for production deployment. Reproducibility is limited by the lack of a pinned dependency file and reliance on external API keys. The repository is actively maintained and highly influential (29k+ stars), making it a valuable reference for anyone building or evaluating RAG systems.