Published 10/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.22 GB | Duration: 2h 22m
Multi-modal RAG Stack: A Hands-on Journey Through Vector Stores, LLM Integration, and Advanced Retrieval Methods
What you'll learn
Build three professional-grade chatbots: Website, SQL, and Multimedia PDF
Master RAG architecture and implementation from fundamentals to advanced techniques
Run and optimize both open-source and commercial LLMs
Implement vector databases and embeddings for efficient information retrieval
Create sophisticated AI applications using LangChain framework
Deploy advanced techniques like prompt caching and query expansion
Understand how to deploy on AWS EC2 (Basic Guide)
Requirements
Basic Python knowledge is Good to have but not mandatory.
Description
Transform your development skills with our comprehensive course on Retrieval-Augmented Generation (RAG) and LangChain. Whether you're a developer looking to break into AI or an experienced programmer wanting to master RAG, this course provides the perfect blend of theory and hands-on practice to help you build production-ready AI applications.What You'll LearnBuild three professional-grade chatbots: Website, SQL, and Multimedia PDFMaster RAG architecture and implementation from fundamentals to advanced techniquesRun and optimize both open-source and commercial LLMsImplement vector databases and embeddings for efficient information retrievalCreate sophisticated AI applications using LangChain frameworkDeploy advanced techniques like prompt caching and query expansionCourse ContentSection 1: RAG FundamentalsUnderstanding Retrieval-Augmented Generation architectureCore components and workflow of RAG systemsBest practices for RAG implementationReal-world applications and use casesSection 2: Large Language Models (LLMs) - Hands-on PracticeSetting up and running open-source LLMs with OllamaModel selection and optimization techniquesPerformance tuning and resource managementPractical exercises with local LLM deploymentSection 3: Vector Databases & EmbeddingsDeep dive into embedding models and their applicationsHands-on implementation of FAISS, ANNOY, and HNSW methodsSpeed vs. accuracy optimization strategiesIntegration with Pinecone managed databasePractical vector visualization and analysisSection 4: LangChain FrameworkText chunking strategies and optimizationLangChain architecture and componentsAdvanced chain composition techniquesIntegration with vector stores and LLMsHands-on exercises with real-world dataSection 5: Advanced RAG TechniquesQuery expansion and optimizationResult re-ranking strategiesPrompt caching implementationPerformance optimization techniquesAdvanced indexing methodsSection 6: Building Production-Ready ChatbotsWebsite ChatbotArchitecture and implementationContent indexing and retrievalResponse generation and optimizationSQL ChatbotNatural language to SQL conversionQuery optimization and safetyDatabase integration best practicesMultimedia PDF ChatbotMulti-modal content processingPDF parsing and indexingRich media response generationWho This Course is ForSoftware developers looking to specialize in AI applicationsAI engineers wanting to master RAG implementationBackend developers interested in building intelligent chatbotsTechnical professionals seeking hands-on LLM experiencePrerequisitesBasic Python programming knowledgeFamiliarity with REST APIsUnderstanding of basic database conceptsBasic understanding of machine learning concepts (helpful but not required)Why Take This CourseIndustry-relevant skills currently in high demandHands-on experience with real-world examplesPractical implementation using Tesla Motors databaseComplete coverage from fundamentals to advanced conceptsProduction-ready code and best practicesWorkshop-tested content with proven resultsWhat You'll BuildBy the end of this course, you'll have built three professional-grade chatbots and gained practical experience with:RAG system implementationVector database integrationLLM optimizationAdvanced retrieval techniquesProduction-ready AI applicationsJoin us on this exciting journey to master RAG and LangChain, and position yourself at the forefront of AI development.
