Learn RAG From Scratch – Python AI Tutorial from a LangChain Engineer

About this video

• Video Title: Learn RAG From Scratch – Python AI Tutorial from a LangChain Engineer
• Channel: freeCodeCamp.org
• Speakers: Lance Martin
• Duration: 02:33:11

Overview

This video provides a comprehensive tutorial on implementing Retrieval-Augmented Generation (RAG) from scratch using Python. Lance Martin, a software engineer at LangChain, explains the core concepts of RAG, its motivation, and various techniques for building RAG pipelines. The course covers indexing, retrieval, generation, query translation (including multi-query, RAG Fusion, decomposition, step-back prompting, and HyDE), routing, query construction, advanced indexing methods (multi-representation, RAPTOR, ColBERT), CRAG, Adaptive RAG, and the future of RAG.

Key takeaways

• RAG Motivation: RAG combines custom data with Large Language Models (LLMs) to overcome the limitations of LLMs trained on public data, allowing them to access and reason over private or external information.
• RAG Core Components: The RAG process involves three main steps: Indexing external data to make it retrievable, Retrieval of relevant documents based on a query, and Generation of answers by an LLM grounded in the retrieved information.
• Query Translation Techniques: Various methods like multi-query, RAG Fusion, decomposition, step-back prompting, and HyDE can be used to rephrase or modify user queries to improve retrieval accuracy.
• Indexing Strategies: Different indexing methods exist, including simple embedding of document chunks, multi-representation indexing (summaries for retrieval, full documents for generation), hierarchical indexing (RAPTOR), and token-level embedding (ColBERT).
• Adaptive RAG and LangGraph: Advanced RAG systems can incorporate "flow engineering" or "adaptive RAG" using frameworks like LangGraph to create state machines that handle complex logic, such as conditional retrieval, grading of documents, web search fallbacks, and hallucination checks, leading to more robust and sophisticated RAG pipelines.
• Future of RAG: The increasing context window sizes of LLMs will influence RAG approaches, potentially shifting focus from precise chunk retrieval to document-centric retrieval and integrating reasoning at multiple stages of the RAG pipeline.