Building RAG Apps with LlamaIndex and JavaScript Online Course
Building RAG Apps with LlamaIndex and JavaScript Online Course
In this course, you will learn to develop Retrieval-Augmented Generation (RAG) applications using LlamaIndex and JavaScript. The course begins with an overview of the structure, prerequisites, and goals, followed by environment setup, including Node.js configuration and OpenAI API integration. You will then explore LlamaIndex fundamentals such as data ingestion, indexing, and querying, building both basic and custom RAG systems. Hands-on projects will teach you to query structured data, handle PDFs, and integrate multiple data sources using an Express API. The course also covers data persistence techniques and deploying production-ready applications, culminating in the creation of a full-stack chatbot app using NextJS. By the end of the course, you will be proficient in building, customizing, and deploying scalable RAG applications.
Key Benefits
- Gain expertise in data ingestion, indexing, and querying using LlamaIndex to build dynamic RAG systems.
- Learn to create and customize RAG applications, progressing from basic setups to implementing complex queries across multiple data sources.
- Develop and deploy a full-featured NextJS web application chatbot integrated with LlamaIndex and OpenAI, ready for production use.
Target Audience
This course is intended for developers with a strong background in JavaScript who wish to dive into the development of RAG systems using LlamaIndex. A solid understanding of Node.js and familiarity with APIs are recommended prerequisites. If you're looking to build scalable, AI-driven applications, this course offers the tools and knowledge to help you succeed.
Learning Objectives
- Develop Retrieval-Augmented Generation (RAG) systems using LlamaIndex and JavaScript.
- Set up and configure a complete development environment tailored for RAG applications.
- Implement data ingestion, indexing, and querying techniques with LlamaIndex.
- Create complex queries in LlamaIndex utilizing custom data loaders and engines.
- Build and customize a full-stack chatbot application using NextJS, LlamaIndex, and OpenAI.
- Deploy scalable and production-ready RAG systems with persistent data for real-world use.
Course Outline
The Building RAG Apps with LlamaIndex and JavaScript Exam covers the following topics -
Module 1 - Introduction
- Overview: Prerequisites and target audience
Module 2 - Development Environment Setup
- Instructions for setting up the development environment with Node.js
- Setting up an OpenAI account and API key
Module 3 - LlamaIndex Deep Dive – Fundamentals
- Detailed exploration of LlamaIndex and its key features
- Crash course on Retrieval-Augmented Generation (RAG)
- Overview of LlamaIndex flow
- Introduction to data ingestion, indexing, and query interfaces in LlamaIndex
- Hands-on: Setting up a basic RAG system with LlamaIndex
Module 4 - LlamaIndex Deep Dive – Main Concepts and Data Loaders
- Core concepts of LlamaIndex and loaders index
- Overview of the querying stage
- Full breakdown of ChatEngine and Querying Engine in the querying stage
- Creating a custom RAG system with LlamaIndex
- Extracting structured data
- Querying data from a PDF file
- Interacting with a RAG system via an Express API
Module 5 - Agents & Advanced Queries with LlamaIndex
- Overview of agents and advanced queries with the RouterQueryEngine
- Creating a RAG system with multiple data sources
- Developing a RouterQueryEngine to manage multiple query engines
- Defining functions and querying tools for agent interactions
Module 6 - Persist Your Data & Production-ready Techniques
- Introduction to production-ready techniques
- Managing data with LlamaIndex
- Loading an index with persisted data and streaming responses
Module 7 - NextJS Full-stack Web Application Chatbot with One Command & Deployment
- Overview of building a full-stack web chatbot application with Next.js
- Generating a full-stack web app using the create-llama CLI command
- Customizing the app with your own data and interacting with it
- Deploying the NextJS full-stack chatbot app to Vercel
