Building RAG Apps with LlamaIndex and JavaScript Practice Exam
Building RAG Apps with LlamaIndex and JavaScript Practice Exam
About Building RAG Apps with LlamaIndex and JavaScript Exam
This exam focuses on assessing your skills in building Retrieval-Augmented Generation (RAG) applications using LlamaIndex (formerly known as GPT Index) and JavaScript. RAG apps use large language models (LLMs) in conjunction with external data sources to enhance the generation of responses, enabling the model to fetch relevant data before generating content. The exam will test your knowledge of setting up and integrating LlamaIndex, working with JavaScript for backend logic, and utilizing external data sources to improve response accuracy and relevance in the applications.
Skills Required
- Ability to set up, configure, and integrate LlamaIndex with different data sources for augmenting model-generated outputs. Understanding how to query and fetch data from databases or APIs for use in RAG applications.
- Strong understanding of JavaScript, particularly in the context of backend development, to handle logic for data retrieval and interaction with language models.
- Knowledge of integrating external APIs and data sources with the LlamaIndex for building RAG applications.
- Basic understanding of NLP concepts and techniques, particularly in the context of enhancing model responses through external data.
- Experience with managing and processing large datasets, including text-based data, to effectively feed into a RAG model for accurate content generation.
- Familiarity with RESTful APIs, SQL/NoSQL databases, and other data integration tools to support the flow of information between the application and the LlamaIndex.
Who should take the Exam?
- This exam is ideal for software developers, machine learning engineers, and data scientists who are looking to build advanced applications that combine the power of language models with external data sources.
- It is specifically designed for individuals who want to enhance their skills in building RAG applications using LlamaIndex and JavaScript.
- Candidates should have a basic to intermediate understanding of JavaScript and programming principles, as well as a foundational knowledge of natural language processing techniques and model integration.
- It is also beneficial for those interested in exploring the intersection of AI/ML and data-driven application development.
- Professionals aiming to work in industries such as AI development, data engineering, and software development for enterprise applications will find this exam beneficial for advancing their careers.
Course Outline
The Building RAG Apps with LlamaIndex and JavaScript Exam covers the following topics -
Domain 1 - Introduction
- Overview: Prerequisites and target audience
Domain 2 - Development Environment Setup
- Instructions for setting up the development environment with Node.js
- Setting up an OpenAI account and API key
Domain 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
Domain 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
Domain 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
Domain 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
Domain 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
