Building RAG and Memory Systems with LangChain

Delve into advanced document processing, retrieval-augmented generation (RAG), and memory management to create intelligent, context-aware AI applications. Gain expertise in document loaders, embeddings, vector stores, and sophisticated RAG pipelines. This course explores the advanced capabilities of LangChain for handling documents, chunking, embeddings, and retrieval-augmented generation. You will learn how to efficiently load and process diverse data sources, implement chunking and embedding strategies, and build scalable vector stores. The course then guides you through designing comprehensive RAG pipelines and integrating memory systems, culminating in the creation of an AI Research Assistant capable of advanced information retrieval and contextual conversation. The course combines in-depth conceptual overviews with practical walkthroughs and quizzes, enabling you to apply advanced techniques in document processing, RAG, and memory. Each topic is illustrated with real-world scenarios to help you build sophisticated, context-aware AI solutions. This course is part two of a three-course Specialization designed to build a complete and cohesive understanding of the subject. While it offers valuable skills on its own, you'll gain the most benefit by progressing through all three courses as a structured learning journey. This course is based on Agentic AI Systems: Build & Deploy with LangChain/LangGraph, by Paulo Dichone. This course is licensed and distributed by Packt. All rights reserved. Packt is one of the world's most prolific publishers of cutting-edge technical content. For over two decades we've made it our mission to curate and publish the knowledge of only the very best technical experts. We focus on real-world courses that help our customers get the job done, with coverage that extends across a wide range of established and cutting-edge technical topics. If you're an individual or an organisation that embraces learning by doing, Packt is the perfect fit for you.