Retrieval Augmented Generation (RAG) for Production with LangChain & LlamaIndex Free Course

welcome everyone today we are pursuing our special collaboration between active Loop todii and the Intel disruptor initiative together we have launched the Gen 360 foundational model certification course in this video we are focusing on the final of the three-part series retrieval augmented generation for production with L index and Lang Ching where you'll learn how to build on rag techniques learned in the first Lang chain and Vector databases in production course in particular you will learn Advanced rag techniques with Lam index how to build rag agents and rag evaluation systems this course will guide you on the optimal methods and practices for getting rag production ready with plenty of Applied industry project examples this course was also made with the support from lendex let's get started we think a large number of human tasks throughout many Industries can be assessed with AI through some combination of llms prompting Rag and fine-tuning work flows we are huge fans of frag as it helps with reducing hallucinations by limiting the llm to answer based on existing documentation helping with explainability error checking and copyright issues by clearly referencing its sources for each comment giving private or specific or more upto-date data to the llm and not relying on more blackbox llms for what the models knows and has memorized we touched upon basic rag in our first Lang chain and Vector databases score but to build more advanced and reliable products Beyond a basic chatbot requires more complex techniques and iterations of the model the rag for production course aims to provide you with both the theoretical knowledge and practical skills necessary to develop products and applications centered on rag the fundamental pillar of our course is the focus on Hands-On learning we strongly believe that real world application and experimentation are crucial for a deep understanding and effective use of the rag techniques in this course you will move Beyond basic rag applications you'll develop these applications with more Advanced Techniques build a rag agents and evaluate rag systems performances this course is designed with a wide audience in mind from beginners in Ai and current machine learning Engineers to students and professionals considering a carer shift into AI please know that only prior knowledge of coding and python is required if you are completely new to the Lang chain L index and Vector database ecosystem we recommend taking our first course as a complement to the knowledge presented in this course you will start learning the basic rag tools such as loading indexing storing and querying in both L chain and Lam index we'll also demystify the two libraries for you to know how to make a good choice when working with frag or other llm applications you will then move towards more advanced rag techniques aimed at surfacing and using more relevant information from your data set we cover techniques such as query expansion transformation reranking and recursive retrieval together with optimization and production tips and techniques with lendex we also introduce how a better embedding management through something like active Loops deep memory can be used to improve accuracy with then progress with the exciting stuff learning how to build rag agents in Lang chain and lendex as well as an introduction to open AI assistants and some other tools and models that can be used in rag products we conclude with a summary of rag evaluation techniques in L index together with an introduction to Lang Smith in Lang chain throughout the course you'll tackle a series of projects specifically designed to provide hands-on experience and deepen your understanding of rag products the course contains over seven code projects across financial analysis biomedical legal and e-commerce verticals you'll create an AI agent that can plan an outfit based on a clothing item you own your budget and even the weather in your area you leverage segment anything or gp4 Vision to build a medical pill recognizer that will help you distinguish similar looking pills and chat with the pharmacological information of the pill you will also be able to build an investor presentation analyzer multimodel rag project finally you'll explore deep memory deep Lakes feature that helps you increase retrial accuracy by up to 22% across biomedical legal and financial use cases you'll become Adept at various strategies for building rag llm products and get a clear perspective on how to apply these to Industry specific data sets and applications this course isn't just a learning experience it's a journey into the heart of rag llm technology you'll Master various strategies gaining a clear actionable perspective on applying these Innovations to specific industry needs get ready to transform the way you think and interact with technology this course is hosted by active Loop and is designed as as a self-based learning Journey we encourage active interaction and engagement within our Learning Community if you have any questions about the course or specific lessons we have a dedicated Forum in the 2 rzi Learn AI together Discord Community this Vibrant Community comprises over 55,000 AI experts and enthusiasts you can join the dedicated Channel using the given link and ask any question you may have in our dedicated geni 360 Channel when you join us we'd love if you could introduce yourself and mention that you are doing the course in case you have deep lake related issues or want to receive more support as you develop your rag use case with deep lake our experts and users are ready to assist you in the Deep Lake slack Channel by participating in this course and completing the quizzes at the end of each chapter you will earn the geni 360 certification a valuable addition to your professional credentials while you have the option to skip the quizzes during the course remember to Circle back and complete them if you aim to secure your certification there are no fees for participating in the course and obtaining certification practical projects may require the use of various tools and platforms to run the code examples in full these will be thoroughly guided in the individual lessons however the main platforms that you will use throughout the course are active Loops Deep Lake open AI Lama index Lang chain and Lang chains lsmith before starting the course it is crucial to have the appropriate code environment ready here are the essential components first install a code editor on your computer a popular choice is Visual Studio code but you can use any code editor you are familiar with Second Use a python virtual environment to manage python libraries and avoid conflicts this is a very good practice to follow and lastly alternatively you can use a Google collab notebook a free cloudbased Jupiter notebook environment but feel free to setep this up yourself these platforms are explained in more detail in the course introduction lesson the Gen 360 course takers are entitled to a 3D plag trial to try out the features from the course on your own projects this is the only way how you would currently get access to deep L managed Tor database and deep memory features which increase accuracy up to 22% while reducing database costs up to 75% once you register for deep lake make sure to sign up for the free trial and use the geni 360 promo code for an extended trial this course is designed to empower you with the skills needed to apply llms in various real world applications below you'll find access to the retrieval augmented generation for production with L index and Lang chain course loaded with all the resources and materials to jump start your in-depth exploration into this field to begin your journey click the link below to dive into the world of rag in production we are thrilled to have you join us in building better and Safer llm applications [Music] [Music]

Channel: What's AI by Louis-François Bouchard

Views: 11,431

Keywords: ai, artificial intelligence, machine learning, deep learning, ml, data science, ainews, ai news, whats ai, whatsai, louis, louis bouchard, bouchard, ai simplified, simple ai, ai explained, ai demystified, demystify ai, explain ai, what's ai

Id: Rw_cjjKncvg

Length: 8min 20sec (500 seconds)

Published: Tue Dec 19 2023