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Building Data-Driven Applications with LlamaIndex

Building Data-Driven Applications with LlamaIndex

By : Andrei Gheorghiu
5 (10)
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Building Data-Driven Applications with LlamaIndex

Building Data-Driven Applications with LlamaIndex

5 (10)
By: Andrei Gheorghiu

Overview of this book

Discover the immense potential of Generative AI and Large Language Models (LLMs) with this comprehensive guide. Learn to overcome LLM limitations, such as contextual memory constraints, prompt size issues, real-time data gaps, and occasional ‘hallucinations’. Follow practical examples to personalize and launch your LlamaIndex projects, mastering skills in ingesting, indexing, querying, and connecting dynamic knowledge bases. From fundamental LLM concepts to LlamaIndex deployment and customization, this book provides a holistic grasp of LlamaIndex's capabilities and applications. By the end, you'll be able to resolve LLM challenges and build interactive AI-driven applications using best practices in prompt engineering and troubleshooting Generative AI projects.
Table of Contents (18 chapters)
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1
Part 1:Introduction to Generative AI and LlamaIndex
4
Part 2: Starting Your First LlamaIndex Project
8
Part 3: Retrieving and Working with Indexed Data
12
Part 4: Customization, Prompt Engineering, and Final Words

LlamaIndex: The Hidden Jewel - An Introduction to the LlamaIndex Ecosystem

Now that you’ve got a solid understanding of what large language models (LLMs) are and what they can (and cannot) do. It’s time to discover how LlamaIndex can take your interactive AI applications to the next level. We’ll explore how retrieval-augmented generation (RAG) using LlamaIndex can provide the missing link between the vast knowledge of LLMs and your proprietary data.

In this chapter, we will cover the following main topics:

  • Optimizing language models – The symbiosis of fine-tuning, RAG, and LlamaIndex
  • Discovering the advantages of progressively disclosing complexity
  • Introducing personalized intelligent tutoring system (PITS) – our hands-on LlamaIndex project
  • Preparing our coding environment
  • Familiarizing ourselves with the structure of the LlamaIndex code repository

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