RAG Masters in 10 Hours with Projects in One Shot Full Course | Euron

Euron - https://euron.one/ Course Link : https://euron.one/course/rag-masters For any queries or counseling, feel free to call or WhatsApp us at: +919110665931 / +919019065931 Ready to dive into the world of Retrieval-Augmented Generation (RAG) pipelines? This beginner-friendly guide walks you through building RAG pipelines step-by-step, making complex concepts simple and practical. Perfect for those starting with RAG or anyone looking to enhance their skills in retrieval-based AI systems, this comprehensive tutorial is your ultimate resource. Here's what you'll learn: Fundamentals of RAG: Understand the core concepts and architecture. How to use tools like vector databases, embeddings, LangChain, and more. Practical examples to apply RAG to real-world scenarios. An introduction to prompt engineering and handling private datasets. Deployment strategies, including Streamlit, Render, and AWS Elastic Beanstalk. Why watch this video? Perfect for beginners exploring RAG and retrieval-based AI. Hands-on coding examples to build your own RAG system. Clear, structured steps to help you follow along and succeed. CHAPTERS: 00:00 - Announcements 01:36 - What is Retrieval Augmented Generation (RA) 05:09 - How RA Works 10:47 - Understanding Retrieval Augmented Generation 14:20 - Problems Solved by RA 21:13 - Overview of RAG Pipeline 22:58 - RAG Pipeline Explained 27:56 - Generating Embeddings 38:35 - Preparing Your Own Data for RAG 41:02 - Creating Text Files for Data 49:53 - Creating Embeddings from Data 1:11:40 - Querying from Vector Database 1:14:47 - Final Operation: How RA Works 1:31:56 - Deploying Code in Streamlit 1:32:51 - Setting Up Application Directory 1:33:14 - Creating app.py File 1:37:00 - Developing app.py 1:41:34 - Creating Environment for Streamlit 1:46:28 - Testing Streamlit Application 1:50:04 - Deploying Application on Streamlit 1:50:34 - Deploying Application on Render 1:50:40 - Deploying Application on AWS Elastic Beanstalk 2:00:24 - Streamlit Deployment Hands-On 2:08:27 - Render Deployment Hands-On 2:27:15 - Final Deployed Application 2:29:54 - Introduction to Document Loading 2:32:41 - Text Loader Overview 2:35:21 - Loading CSV Files 2:36:00 - Loading PDF Files 2:39:53 - Loading with BevBase 2:46:29 - Chunking and Splitting Data 3:05:14 - Lecture 2 Overview 3:10:28 - Cosine Similarity and Normalization 3:19:59 - Practical Cosine Similarity 3:26:01 - Introduction to Vector Databases 3:31:52 - Understanding Vector Representation 3:37:19 - Cosine Similarity Explained 4:11:28 - Step 2: Creating Embeddings 4:18:25 - Step 3: Creating Embedding Arrays 4:40:29 - Inserting Data into ChromaDB 4:43:14 - Querying ChromaDB 4:46:38 - Updating Records in ChromaDB 4:49:24 - Adding Metadata Information 4:56:22 - Persisting Collections in ChromaDB 5:03:20 - Pinecone Insert Operations 5:26:14 - Vaviet Overview 5:31:16 - Connecting to BayesVector 6:13:20 - Lecture 2: End to End ALM Chain 6:14:53 - ALM Chain with Querent Overview 6:18:44 - Project Setup Process 6:24:08 - System Setup for ALM Chain 6:25:36 - Accessing LM and Embeddings 7:10:07 - Multi-Agent System with Self-Routing 7:12:05 - Accessing LLM in ALM 7:19:55 - Creating a Tool for ALM 7:22:08 - Creating an Agent in ALM 7:23:41 - Creating a Routing Agent 7:34:01 - Introduction to (LCEL) 8:03:02 - Setting the Entry Point in ALM 8:10:33 - Multi-Agent System Overview 8:16:08 - Creating Context Files 8:18:37 - Researcher Node in ALM 8:24:45 - Synthesizer Node Overview 8:27:20 - Classifier Node in ALM 8:28:45 - Finalizer Node Overview 8:42:01 - Understanding Prompting Techniques 8:43:20 - Crafting Effective Prompts 8:53:20 - Few-Shot Prompting Techniques 9:00:48 - Output Format Instructions 9:04:27 - Chain of Thought (COT) Prompting 9:09:24 - Explicit Anchoring Techniques 9:50:37 - Project Setup Process 9:54:41 - Obtaining URI API Key 9:57:48 - Storing and Retrieving Vectors 10:43:58 - Deploying the Chatbot Application 10:45:58 - Testing the Deployed Chatbot Roadmap for you : AI /Data Science Pro Level Expert Roadmap - https://euron.one/roadmap/c9361831-c8... NLP expert Roadmap - https://euron.one/roadmap/300bc526-ed... Data Analytics / Business Analytics Expert Roadmap - https://euron.one/roadmap/920278e8-e3... Big Data / Data Engineering Expert Roadmap - https://euron.one/roadmap/98c8db49-2e... Computer Vision Roadmap - https://euron.one/roadmap/d8281277-5c... Deep Learning Roadmap - https://euron.one/roadmap/1495a7ba-42... Generative AI Roadmap - https://euron.one/roadmap/2380f611-74... Machine Learning Expert Roadmap - https://euron.one/roadmap/ff514391-32... Android- https://play.google.com/store/apps/de... IOS - https://apps.apple.com/in/app/euron-y...