Hands-On AI: RAG using LlamaIndex

سرفصل های دوره

This course offers a deep dive into the principles and applications of retrieval-augmented generation (RAG), with a focus on the innovative LlamaIndex system. Explore how RAG enhances machine learning models by integrating external knowledge sources for more informed and accurate outputs. Instructor Harpreet Sahota covers the architecture of retrieval systems, the mechanics of indexing vast datasets, and the integration of LlamaIndex with AI models.

Gain an understanding of the theoretical underpinnings of RAG, practical skills in building and deploying LlamaIndex, and review a critical analysis of RAG applications in various industries. Topics range from the basics of data retrieval and indexing to advanced techniques in enhancing generative models with external data. By the end of the course, you’ll be prepared to design, implement, and evaluate RAG systems, positioning them at the cutting edge of AI technology implementation.

01 - Introduction

01 - Overcome the limitations of LLMs with RAG

02 - Limitations of LLMs

03 - Use cases for retrieval-augmented generation (RAG)

02 - 1. Getting Started

01 - Using GitHub Codespaces

02 - Setting up your environment

03 - Choosing an LLM and embeddings provider

04 - Setting up LLM accounts

05 - Choosing a vector database

06 - Setting up a Qdrant account

07 - Downloading our data

03 - 2. Fundamental Concepts in LlamaIndex

01 - How LlamaIndex is organized

02 - Using LLMs

03 - Loading data

04 - Indexing

05 - Storing and retrieving

06 - Querying

07 - Agents

04 - 3. Introduction to RAG

01 - Components of a RAG system

02 - Ingestion pipeline

03 - Query pipeline

04 - Prompt engineering for RAG

05 - Data preparation for RAG

06 - Putting it all together

07 - Drawbacks of Naive RAG

05 - 4. RAG Evaluation

01 - Introduction to RAG evaluation

02 - Evaluation metrics

03 - How to create an evaluation set

06 - 5. Advanced RAG Pre-Retrieval and Indexing Techniques

01 - How we can improve on Naive RAG

02 - Optimizing chunk size

03 - Small to big retrieval

04 - Semantic chunking

05 - Metadata extraction

06 - Document summary index

07 - Query transformation

07 - 6. Advanced RAG Post-Retrieval and Other Techniques

01 - Node post-processing

02 - Re-ranking

03 - FLARE

04 - Prompt compression

05 - Self-correcting

08 - 7. Modular RAG

01 - Hybrid retrieval

02 - Agentic RAG

03 - Ensemble retrieval

04 - Ensemble query engine

09 - Conclusion

01 - LlamaIndex evaluation

02 - Comparative analysis of retrieval-augmented generation techniques

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تولید کننده: LinkedIn Learning (Lynda)