Building Data-Driven Applications with LlamaIndex : A Practical Guide to Retrieval-Augmented Generation (RAG) to Enhance LLM Applications

Building Data-Driven Applications with LlamaIndex A Practical Guide to Retrieval-Augmented Generation (RAG) to Enhance LLM Applications

"Solve real-world problems easily with artificial intelligence (AI) using the LlamaIndex data framework to enhance your LLM-based Python applications Key Features Examine text chunking effects on RAG workflows and understand security in RAG app development Discover chatbots and agents and learn...

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Detalles Bibliográficos
Otros Autores: Gheorghiu, Andrei, author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Birmingham, England : Packt Publishing Ltd [2024]
Edición:First edition
Materias:
Natural language generation (Computer science) > Computer programs.
Web applications > Development.
Artificial intelligence > Computer programs.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009820417906719
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright and Credits
  • Dedicated
  • Contributors
  • Table of Contents
  • Preface
  • Part 1: Introduction to Generative AI and LlamaIndex
  • Chapter 1: Understanding Large Language Models
  • Introducing GenAI and LLMs
  • What is GenAI?
  • What is an LLM?
  • 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
  • Is RAG the only possible solution?
  • What LlamaIndex does
  • Discovering the advantages of progressively disclosing complexity
  • An important aspect to consider
  • Introducing PITS - our LlamaIndex hands-on project
  • Here's how it will work
  • Preparing our coding environment
  • Installing Python
  • Installing Git
  • Installing LlamaIndex
  • Signing up for an OpenAI API key
  • Discovering Streamlit - the perfect tool for rapid building and deployment!
  • Installing Streamlit
  • Finishing up
  • One final check
  • 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
  • Documents
  • Nodes
  • Manually creating the Node objects
  • Automatically extracting Nodes from Documents using splitters
  • Nodes don't like to be alone - they crave relationships
  • Why are relationships important?
  • Indexes
  • Are we there yet?
  • How does this actually work under the hood?
  • A quick recap of the key concepts
  • Building our first interactive, augmented LLM application.
  • Using the logging features of LlamaIndex to understand the logic and debug our applications
  • Customizing the LLM used by LlamaIndex
  • Easy as 1-2-3
  • The temperature parameter
  • Understanding how Settings can be used for customization
  • Starting our PITS project - hands-on exercise
  • Let's have a look at the source code
  • 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
  • Ingesting data from a web page
  • Ingesting data from a database
  • Bulk-ingesting data from sources with multiple file formats
  • Parsing the documents into nodes
  • Understanding the simple text splitters
  • Using more advanced node parsers
  • Using relational parsers
  • Confused about node parsers and text splitters?
  • Understanding chunk_size and chunk_overlap
  • Including relationships with include_prev_next_rel
  • Practical ways of using these node creation models
  • Working with metadata to improve the context
  • SummaryExtractor
  • QuestionsAnsweredExtractor
  • TitleExtractor
  • EntityExtractor
  • KeywordExtractor
  • PydanticProgramExtractor
  • MarvinMetadataExtractor
  • Defining your custom extractor
  • Is having all that metadata always a good thing?
  • Estimating the potential cost of using metadata extractors
  • Follow these simple best practices to minimize your costs
  • Estimate your maximal costs before running the actual extractors
  • Preserving privacy with metadata extractors, and not only
  • Scrubbing personal data and other sensitive information
  • 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
  • Common features of all Index types
  • Understanding the VectorStoreIndex
  • A simple usage example for the VectorStoreIndex
  • Understanding embeddings
  • Understanding similarity search
  • OK, but how does LlamaIndex generate these embeddings?
  • How do I decide which embedding model I should use?
  • Persisting and reusing Indexes
  • Understanding the StorageContext
  • The difference between vector stores and vector databases
  • Exploring other index types in LlamaIndex
  • The SummaryIndex
  • The DocumentSummaryIndex
  • The KeywordTableIndex
  • The TreeIndex
  • The KnowledgeGraphIndex
  • Building Indexes on top of other Indexes with ComposableGraph
  • How to use the ComposableGraph
  • A more detailed description of this concept
  • 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
  • The VectorStoreIndex retrievers
  • The DocumentSummaryIndex retrievers
  • The TreeIndex retrievers
  • The KnowledgeGraphIndex retrievers
  • Common characteristics shared by all retrievers
  • Efficient use of retrieval mechanisms - asynchronous operation
  • Building more advanced retrieval mechanisms
  • The naive retrieval method
  • Implementing metadata filters
  • Using selectors for more advanced decision logic
  • Understanding tools
  • Transforming and rewriting queries
  • Creating more specific sub-queries
  • Understanding the concepts of dense and sparse retrieval
  • Dense retrieval
  • Sparse retrieval
  • Implementing sparse retrieval in LlamaIndex
  • Discovering other advanced retrieval methods
  • Summary.
  • Chapter 7: Querying Our Data, Part 2 - Postprocessing and Response Synthesis
  • Technical requirements
  • Re-ranking, transforming, and filtering nodes using postprocessors
  • Exploring how postprocessors filter, transform, and re-rank nodes
  • SimilarityPostprocessor
  • KeywordNodePostprocessor
  • PrevNextNodePostprocessor
  • LongContextReorder
  • PIINodePostprocessor and NERPIINodePostprocessor
  • MetadataReplacementPostprocessor
  • SentenceEmbeddingOptimizer
  • Time-based postprocessors
  • Re-ranking postprocessors
  • Final thoughts about node postprocessors
  • Understanding response synthesizers
  • Implementing output parsing techniques
  • Extracting structured outputs using output parsers
  • Extracting structured outputs using Pydantic programs
  • Building and using query engines
  • Exploring different methods of building query engines
  • Advanced uses of the QueryEngine interface
  • Hands-on - building quizzes in PITS
  • Summary
  • Chapter 8: Building Chatbots and Agents with LlamaIndex
  • Technical requirements
  • Understanding chatbots and agents
  • Discovering ChatEngine
  • Understanding the different chat modes
  • Implementing agentic strategies in our apps
  • Building tools and ToolSpec classes for our agents
  • Understanding reasoning loops
  • OpenAIAgent
  • ReActAgent
  • How do we interact with agents?
  • Enhancing our agents with the help of utility tools
  • Using the LLMCompiler agent for more advanced scenarios
  • Using the low-level Agent Protocol API
  • 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
  • How LLaMA and LLaMA 2 changed the open source landscape
  • Running a local LLM using LM Studio.
  • Routing between LLMs using services such as Neutrino or OpenRouter
  • What about customizing embedding models?
  • Leveraging the Plug and Play convenience of using Llama Packs
  • Using the Llama CLI
  • Using advanced tracing and evaluation techniques
  • Tracing our RAG workflows using Phoenix
  • Evaluating our RAG system
  • Introduction to deployment with Streamlit
  • HANDS-ON - a step-by-step deployment guide
  • Deploying our PITS project on Streamlit Community Cloud
  • 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
  • Using advanced prompting techniques in LlamaIndex
  • The golden rules of prompt engineering
  • Accuracy and clarity in expression
  • Directiveness
  • Context quality
  • Context quantity
  • Required output format
  • Inference cost
  • Overall system latency
  • Choosing the right LLM for the task
  • Common methods used for creating effective prompts
  • Summary
  • Chapter 11: Conclusion and Additional Resources
  • Other projects and further learning
  • The LlamaIndex examples collection
  • Moving forward - Replit bounties
  • The power of many - the LlamaIndex community
  • Key takeaways, final words, and encouragement
  • On the future of RAG in the larger context of generative AI
  • A small philosophical nugget for you to consider
  • Summary
  • Index
  • Other Books You May Enjoy.

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