Building Your First RAG Model using LlamaIndex
This course will guide you through building your first Retrieval-Augmented Generation (RAG) system using LlamaIndex. You will start with data ingestion by loading a file into the system, followed by indexing the data for efficient retrieval. Next, you will set up retrieval configurations and use a response synthesizer to combine data into a coherent response. Finally, you will employ a query engine to generate responses. By the end of this course, you will have a solid understanding of these processes and be able to build an RAG system using LlamaIndex code effectively.
Course curriculum
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1
Introduction to RAG systems
- Welcome to this course
- Why RAG
- What is RAG system
- Overview of RAG Framework
- Quiz
- Course handouts
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2
Getting Started with LlamaIndex
- Introduction to LlamaIndex
- Components of LlamaIndex
- Reading Material: How to get your API Key
- How to get Open AI Keys - 2 min - Website go through
- Build Your First RAG system using LlamaIndex
- Quiz
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3
Component of LlamaIndex
- Data Loaders
- Data Loaders Implementation
- Chunking - Tokenization
- Chunking _ Tokenization Implementation
- Node Parser
- Node Parser Implementation part -1
- Node Parser Implementation part- 2
- Node Parser Implementation part- 3
- Node Parser Implementation part- 4
- Embeddings
- Embeddings Implementation
- Quiz
Certificate of Completion
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About the Instructor
Prashant Sahu, Ph.D IIT Bombay; Data Science Manager, Analytics Vidhya
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Key Takeaways from the course
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Learn the steps involved in building a RAG system using Llamaindex.
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Hands-On Experience: Engage with exercises designed to reinforce your learning and apply concepts in real-world scenarios.