Building Data-Driven Applications with LlamaIndex, 1st Edition

Part 1:Introduction to Generative AI and LlamaIndex
Chapter 1: Understanding Large Language Models
Introducing GenAI and LLMs
Understanding the role of LLMs in modern technology
Exploring challenges with LLMs
Augmenting LLMs with RAG
Summary
Chapter 2: LlamaIndex: The Hidden Jewel - An Introduction to the LlamaIndex Ecosystem
Technical requirements
Optimizing language models – the symbiosis of fine-tuning, RAG, and LlamaIndex
Discovering the advantages of progressively disclosing complexity
Introducing PITS – our LlamaIndex hands-on project
Preparing our coding environment
Familiarizing ourselves with the structure of the LlamaIndex code repository
Summary
Part 2: Starting Your First LlamaIndex Project
Chapter 3: Kickstarting Your Journey with LlamaIndex
Technical requirements
Uncovering the essential building blocks of LlamaIndex – documents, nodes, and indexes
Building our first interactive, augmented LLM application
Starting our PITS project – hands-on exercise
Summary
Chapter 4: Ingesting Data into Our RAG Workflow
Technical requirements
Ingesting data via LlamaHub
An overview of LlamaHub
Using the LlamaHub data loaders to ingest content
Parsing the documents into nodes
Working with metadata to improve the context
Estimating the potential cost of using metadata extractors
Preserving privacy with metadata extractors, and not only
Using the ingestion pipeline to increase efficiency
Handling documents that contain a mix of text and tabular data
Hands-on – ingesting study materials into our PITS
Summary
Chapter 5: Indexing with LlamaIndex
Technical requirements
Indexing data – a bird’s-eye view
Understanding the VectorStoreIndex
Persisting and reusing Indexes
Exploring other index types in LlamaIndex
Building Indexes on top of other Indexes with ComposableGraph
Estimating the potential cost of building and querying Indexes
Indexing our PITS study materials – hands-on
Summary
Part 3: Retrieving and Working with Indexed Data
Chapter 6: Querying Our Data, Part 1 – Context Retrieval
Technical requirements
Learning about query mechanics – an overview
Understanding the basic retrievers
Building more advanced retrieval mechanisms
Understanding the concepts of dense and sparse retrieval
Summary
Chapter 7: Querying Our Data, Part 2 – Postprocessing and Response Synthesis
Technical requirements
Re-ranking, transforming, and filtering nodes using postprocessors
Understanding response synthesizers
Implementing output parsing techniques
Building and using query engines
Hands-on – building quizzes in PITS
Summary
Chapter 8: Building Chatbots and Agents with LlamaIndex
Technical requirements
Understanding chatbots and agents
Implementing agentic strategies in our apps
Hands-on – implementing conversation tracking for PITS
Summary
Part 4: Customization, Prompt Engineering, and Final Words
Chapter 9: Customizing and Deploying Our LlamaIndex Project
Technical requirements
Customizing our RAG components
Using advanced tracing and evaluation techniques
Introduction to deployment with Streamlit
HANDS-ON – a step-by-step deployment guide
Summary
Chapter 10: Prompt Engineering Guidelines and Best Practices
Technical requirements
Why prompts are your secret weapon
Understanding how LlamaIndex uses prompts
Customizing default prompts
The golden rules of prompt engineering
Summary
Chapter 11: Conclusion and Additional Resources
Other projects and further learning
Key takeaways, final words, and encouragement
Summary
Index
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