book

Building Data-Driven Applications with LlamaIndex

Name: Building Data-Driven Applications with LlamaIndex
Author: Andrei Gheorghiu
ISBN: 9781835089507

by Andrei Gheorghiu

May 2024

Intermediate to advanced

368 pages

10h 7m

English

Packt Publishing

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Building Data-Driven Applications with LlamaIndex
ContributorsAbout the authorAbout the reviewers
Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesConventions usedGet in touchShare Your ThoughtsDownload a free PDF copy of this book
Part 1:Introduction to Generative AI and LlamaIndex
Chapter 1: Understanding Large Language Models
Introducing GenAI and LLMsWhat is GenAI?What is an LLM?Understanding the role of LLMs in modern technologyExploring challenges with LLMsAugmenting LLMs with RAGSummary
Chapter 2: LlamaIndex: The Hidden Jewel - An Introduction to the LlamaIndex Ecosystem
Technical requirementsOptimizing language models – the symbiosis of fine-tuning, RAG, and LlamaIndexIs RAG the only possible solution?What LlamaIndex doesDiscovering the advantages of progressively disclosing complexityAn important aspect to considerIntroducing PITS – our LlamaIndex hands-on projectHere’s how it will workPreparing our coding environmentInstalling PythonInstalling GitInstalling LlamaIndexSigning up for an OpenAI API keyDiscovering Streamlit – the perfect tool for rapid building and deployment!Installing StreamlitFinishing upOne final checkFamiliarizing ourselves with the structure of the LlamaIndex code repositorySummary
Part 2: Starting Your First LlamaIndex Project
Chapter 3: Kickstarting Your Journey with LlamaIndex
Technical requirementsUncovering the essential building blocks of LlamaIndex – documents, nodes, and indexesDocumentsNodesManually creating the Node objectsAutomatically extracting Nodes from Documents using splittersNodes don’t like to be alone – they crave relationshipsWhy are relationships important?IndexesAre we there yet?How does this actually work under the hood?A quick recap of the key conceptsBuilding our first interactive, augmented LLM applicationUsing the logging features of LlamaIndex to understand the logic and debug our applicationsCustomizing the LLM used by LlamaIndexEasy as 1-2-3The temperature parameterUnderstanding how Settings can be used for customizationStarting our PITS project – hands-on exerciseLet’s have a look at the source codeSummary
Chapter 4: Ingesting Data into Our RAG Workflow
Technical requirementsIngesting data via LlamaHubAn overview of LlamaHubUsing the LlamaHub data loaders to ingest contentIngesting data from a web pageIngesting data from a databaseBulk-ingesting data from sources with multiple file formatsParsing the documents into nodesUnderstanding the simple text splittersUsing more advanced node parsersUsing relational parsersConfused about node parsers and text splitters?Understanding chunk_size and chunk_overlapIncluding relationships with include_prev_next_relPractical ways of using these node creation modelsWorking with metadata to improve the contextSummaryExtractorQuestionsAnsweredExtractorTitleExtractorEntityExtractorKeywordExtractorPydanticProgramExtractorMarvinMetadataExtractorDefining your custom extractorIs having all that metadata always a good thing?Estimating the potential cost of using metadata extractorsFollow these simple best practices to minimize your costsEstimate your maximal costs before running the actual extractorsPreserving privacy with metadata extractors, and not onlyScrubbing personal data and other sensitive informationUsing the ingestion pipeline to increase efficiencyHandling documents that contain a mix of text and tabular dataHands-on – ingesting study materials into our PITSSummary
Chapter 5: Indexing with LlamaIndex
Technical requirementsIndexing data – a bird’s-eye viewCommon features of all Index typesUnderstanding the VectorStoreIndexA simple usage example for the VectorStoreIndexUnderstanding embeddingsUnderstanding similarity searchOK, but how does LlamaIndex generate these embeddings?How do I decide which embedding model I should use?Persisting and reusing IndexesUnderstanding the StorageContextThe difference between vector stores and vector databasesExploring other index types in LlamaIndexThe SummaryIndexThe DocumentSummaryIndexThe KeywordTableIndexThe TreeIndexThe KnowledgeGraphIndexBuilding Indexes on top of other Indexes with ComposableGraphHow to use the ComposableGraphA more detailed description of this conceptEstimating the potential cost of building and querying IndexesIndexing our PITS study materials – hands-onSummary
Part 3: Retrieving and Working with Indexed Data

Chapter 6: Querying Our Data, Part 1 – Context Retrieval
Technical requirementsLearning about query mechanics – an overviewUnderstanding the basic retrieversThe VectorStoreIndex retrieversThe DocumentSummaryIndex retrieversThe TreeIndex retrieversThe KnowledgeGraphIndex retrieversCommon characteristics shared by all retrieversEfficient use of retrieval mechanisms – asynchronous operationBuilding more advanced retrieval mechanismsThe naive retrieval methodImplementing metadata filtersUsing selectors for more advanced decision logicUnderstanding toolsTransforming and rewriting queriesCreating more specific sub-queriesUnderstanding the concepts of dense and sparse retrievalDense retrievalSparse retrievalImplementing sparse retrieval in LlamaIndexDiscovering other advanced retrieval methodsSummary
Chapter 7: Querying Our Data, Part 2 – Postprocessing and Response Synthesis
Technical requirementsRe-ranking, transforming, and filtering nodes using postprocessorsExploring how postprocessors filter, transform, and re-rank nodesSimilarityPostprocessorKeywordNodePostprocessorPrevNextNodePostprocessorLongContextReorderPIINodePostprocessor and NERPIINodePostprocessorMetadataReplacementPostprocessorSentenceEmbeddingOptimizerTime-based postprocessorsRe-ranking postprocessorsFinal thoughts about node postprocessorsUnderstanding response synthesizersImplementing output parsing techniquesExtracting structured outputs using output parsersExtracting structured outputs using Pydantic programsBuilding and using query enginesExploring different methods of building query enginesAdvanced uses of the QueryEngine interfaceHands-on – building quizzes in PITSSummary
Chapter 8: Building Chatbots and Agents with LlamaIndex
Technical requirementsUnderstanding chatbots and agentsDiscovering ChatEngineUnderstanding the different chat modesImplementing agentic strategies in our appsBuilding tools and ToolSpec classes for our agentsUnderstanding reasoning loopsOpenAIAgentReActAgentHow do we interact with agents?Enhancing our agents with the help of utility toolsUsing the LLMCompiler agent for more advanced scenariosUsing the low-level Agent Protocol APIHands-on – implementing conversation tracking for PITSSummary
Part 4: Customization, Prompt Engineering, and Final Words
Chapter 9: Customizing and Deploying Our LlamaIndex Project
Technical requirementsCustomizing our RAG componentsHow LLaMA and LLaMA 2 changed the open source landscapeRunning a local LLM using LM StudioRouting between LLMs using services such as Neutrino or OpenRouterWhat about customizing embedding models?Leveraging the Plug and Play convenience of using Llama PacksUsing the Llama CLIUsing advanced tracing and evaluation techniquesTracing our RAG workflows using PhoenixEvaluating our RAG systemIntroduction to deployment with StreamlitHANDS-ON – a step-by-step deployment guideDeploying our PITS project on Streamlit Community CloudSummary
Chapter 10: Prompt Engineering Guidelines and Best Practices
Technical requirementsWhy prompts are your secret weaponUnderstanding how LlamaIndex uses promptsCustomizing default promptsUsing advanced prompting techniques in LlamaIndexThe golden rules of prompt engineeringAccuracy and clarity in expressionDirectivenessContext qualityContext quantityRequired output formatInference costOverall system latencyChoosing the right LLM for the taskCommon methods used for creating effective promptsSummary
Chapter 11: Conclusion and Additional Resources
Other projects and further learningThe LlamaIndex examples collectionMoving forward – Replit bountiesThe power of many – the LlamaIndex communityKey takeaways, final words, and encouragementOn the future of RAG in the larger context of generative AIA small philosophical nugget for you to considerSummary
Index
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Overview

This hands-on guide takes you through the process of building intelligent, data-driven applications using LlamaIndex, a powerful framework for RAG workflows. You'll learn to enhance large language models (LLMs) by solving common challenges like data retrieval and contextual memory. By the end, you will know how to build effective AI-powered systems tailored to specific scenarios.

What this Book will help me do

Gain fluency in the LlamaIndex ecosystem for RAG workflows.
Discover techniques to index and query data effectively.
Understand how to customize and deploy LlamaIndex solutions.
Follow step-by-step examples to build interactive AI applications.
Master best practices for prompt engineering in LLM projects.

Author(s)

Andrei Gheorghiu is an experienced software engineer specializing in AI-driven systems and natural language processing. With a passion for sharing knowledge, Andrei has guided professionals in adopting RAG workflows through workshops and publications. His practical approach focuses on empowering developers to build efficient, scalable LLM solutions.

Who is it for?

This book is designed for Python developers with a foundational understanding of natural language processing and large language models who aim to implement RAG systems. People who already work in conversational AI or are experienced developers desiring to enhance their skill set in LLM applications will find valuable insights. Beginners who are clear on the foundational concepts of Python and AI will also be able to gain a lot from this organized guide. The book serves as a practical tool for experts looking to reinforce their LlamaIndex implementation skills.

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