Mastering Retrieval-Augmented Generation (RAG)

[mitsumi]

Mastering Retrieval-Augmented Generation (RAG)



Published 11/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 957.04 MB | Duration: 2h 30m

Master RAG from Zero to Hero: Build Real-World AI with Retrieval-Augmented Generation


What you'll learn
Core principles of Retrieval-Augmented Generation (RAG) - Understand how RAG combines retrieval and generation for improved AI responses.
Implementing basic and advanced RAG architectures - Step-by-step guides to setting up RAG, Multi-Query RAG, RAG Fusion, and HyDE RAG.
Working with OpenAI embeddings and Pinecone - Practical exercises in connecting embeddings with vector databases for efficient retrieval.
Multi-query and RAG Fusion techniques - Learn strategies for better, contextually accurate answers through fusion and multi-query models.
Building and deploying RAG with FastAPI on Google Cloud Platform (GCP) - End-to-end deployment guidance for scalable RAG applications.
Prompt routing and database management - Gain experience with routing strategies and optimized content indexing for more efficient RAG systems.
Prompt caching and optimization techniques - Discover ways to reduce costs and improve response speed with caching in RAG models.
Requirements
Basic knowledge of Python programming
Prompt Engineering: Writing basic to intermediate prompts
Understanding of machine learning fundamentals
Description
Welcome to "Mastering Retrieval-Augmented Generation (RAG): From Zero to Hero"!This course is your all-in-one guide to understanding and implementing Retrieval-Augmented Generation (RAG) - a game-changing approach to enhance AI responses with powerful retrieval capabilities. Through hands-on projects, real-world exercises, and step-by-step tutorials, you'll quickly learn how to leverage RAG architectures to build effective and scalable AI solutions.This course is designed for AI practitioners, data scientists, machine learning engineers, and developers with a background in Python programming and a basic understanding of machine learning and NLP concepts.What You'll Learn:- Core RAG Architecture - Understand how RAG works, from basic concepts to advanced multi-query, Fusion, and HyDE architectures.- OpenAI Embeddings and Pinecone Integration - Learn how to connect OpenAI embeddings with Pinecone for efficient content retrieval.- Building RAG Models from Scratch - Implement multi-query and Fusion RAG models with hands-on exercises.- Advanced RAG Techniques - Explore database and prompt routing, caching, and deployment for optimized RAG solutions.- Deploying on Google Cloud Platform (GCP) with FastAPI - Deploy your RAG models in a scalable cloud environment with detailed deployment instructions. Who This Course is For:This course is ideal for those with a background in software engineering, Python programming, and basic ML knowledge who are eager to dive into RAG applications. It's packed with exercises to build your expertise from scratch, making it suitable for those new to RAG while being comprehensive enough for seasoned AI practitioners looking to expand their skills.Join us and become proficient in RAG, from setting up basic architectures to deploying scalable, real-world AI solutions!
Overview
Section 1: Introduction
Lecture 1 Welcome to the RAG Masterclass course!
Lecture 2 How to follow the course?
Lecture 3 Where to find materials?
Section 2: Introduction to RAGs
Lecture 4 What is Retrieval Augmented Generation (RAG)?
Lecture 5 What are Text Embeddings and how to use them?
Lecture 6 Building an old, deterministic F&Q chatbot
Lecture 7 How to use OpenAI's Embedding API?
Lecture 8[EXERCISE] How to find similar text by using Text Similarity - part 1
Lecture 9[SOLUTION] How to find similar text by using Cosine Similarity - part 2
Lecture 10 Building a simple Chatbot using OpenAI's API
Lecture 11[EXERCISE] Building our first RAG-Based Chatbot! - part 1
Lecture 12[SOLUTION] Building our first RAG-Based Chatbot! - part 2
Lecture 13 What are Vector Databases and where to store our vectors?
Lecture 14 Introduction to Pinecone (Vector Database)
Lecture 15 Putting everything together - Building a RAG with external data base - part 1
Lecture 16 Putting everything together - Building a RAG with external data base - part 2
Section 3: Advanced RAGs: User query manipulation
Lecture 17[EXERCISE] What is Multi-Query RAG?
Lecture 18[SOLUTION] Building Multi-Query RAG from scratch
Lecture 19[EXERCISE] What is Fusion RAG?
Lecture 20[SOLUTION] Building Fusion RAG from scratch
Lecture 21[EXERCISE] What is HyDE RAG?
Lecture 22[SOLUTION] Building HyDE RAG from scratch
Section 4: Advanced RAGs: Flow Routing
Lecture 23[EXERCISE] What is Prompt Flow Routing RAG?
Lecture 24[SOLUTION] Implementing Prompt Flow Routing RAG from scratch
Lecture 25[EXERCISE] What is Database Flow Routing RAG?
Lecture 26[SOLUTION - part1] Implementing Database Flow Routing RAG
Lecture 27[SOLUTION - part2] Implementing Database Flow Routing RAG
Section 5: Deploying RAGs to the cloud
Lecture 28 RAG Deployment Code Walkthrough
Lecture 29 What is Prompt Caching?
Lecture 30 Testing the RAG Deployment locally - Using Docker
Lecture 31 Deploying RAG to GCP Cloud Run
AI practitioners who want to deepen their expertise in Retrieval-Augmented Generation (RAG) and apply it to enhance AI-driven solutions.,Machine learning engineers looking to implement advanced RAG techniques like multi-query and RAG Fusion to improve model performance.,Software engineers seeking to expand their skills by building and deploying RAG models using tools like Pinecone and FastAPI.,Data scientists interested in integrating RAG architectures into data-heavy applications for more effective information retrieval and generation.,Developers working with OpenAI embeddings and vector databases who want hands-on practice connecting these tools within a RAG framework.,Professionals aiming to deploy machine learning models on Google Cloud Platform (GCP) with an emphasis on scalability and efficient architecture.,Learners eager to master indexing, prompt routing, and caching techniques to create optimized, cost-effective RAG-powered applications.
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