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  • Book Overview & Buying Building Data-Driven Applications with LlamaIndex
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Building Data-Driven Applications with LlamaIndex

Building Data-Driven Applications with LlamaIndex

By : Andrei Gheorghiu
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Building Data-Driven Applications with LlamaIndex

Building Data-Driven Applications with LlamaIndex

5 (10)
By: Andrei Gheorghiu
Buy this Book

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

The final step before sending our hard-worked contextual data to the LLM is the response synthesizer. It’s the component that’s responsible for generating responses from a language model using a user query and the retrieved context.

It simplifies the process of querying an LLM and synthesizing an answer across our proprietary data. Just like the other components of the framework, response synthesizers can be used on their own or configured in query engines to handle the final step of response generation after nodes have been retrieved and postprocessed.

Here’s a simple example demonstrating how to use one directly on a given set of nodes:

from llama_index.core.schema import TextNode, NodeWithScore
from llama_index.core import get_response_synthesizer
nodes = [
    TextNode(text=
        "The town square clock was built in 1895"
   &...

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