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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

Summary

In this chapter, we explored various indexing strategies and architectures within LlamaIndex. Indexes provide essential capabilities for building performant RAG systems.

Throughout the chapter, we looked at the VectorStoreIndex, which is the most commonly used Index type. We also gained an understanding of embeddings, vector stores, similarity search, and storage contexts. These are key concepts related to the VectorStoreIndex.

We also covered other Index types such as SummaryIndex for simple linear scans, KeywordTableIndex for keyword search, TreeIndex for hierarchical data, and KnowledgeGraphIndex for relationship-based queries. ComposableGraph was introduced as a tool for building multi-level Indexes, and cost estimation techniques were discussed together with best practices.

Overall, this chapter provided an overview of indexing capabilities in LlamaIndex, laying the foundation for building sophisticated and efficient RAG applications.

See you in Chapter 6,...

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