| Sumario: | In this course, you will explore the development of Retrieval-Augmented Generation (RAG) applications using LlamaIndex and JavaScript. You'll start with an introduction to the course structure, prerequisites, and project goals. Initial sections focus on setting up the development environment, including configuring Node.js and obtaining OpenAI API keys to facilitate seamless interaction with LlamaIndex. Next, you'll delve into LlamaIndex fundamentals, covering data ingestion, indexing, and querying. Through hands-on sessions, you'll build basic and custom RAG systems, query structured data, and interact with LlamaIndex using an Express API. These practical exercises will equip you with the skills to handle complex scenarios, such as querying PDF files and integrating multiple data sources. The final sections focus on advanced topics, including managing data persistence and deploying production-ready applications. You'll learn to create a full-stack chatbot app with NextJS, utilizing the create-llama CLI for rapid setup and customization. By course end, you'll be able to build, customize, and deploy scalable RAG applications with confidence.
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