Building Data Driven Applications with LlamaIndex: A Practical Guide on Retrieval Augmented Generation (RAG) for Enhancing LLM Applications
by: Andrei Gheorghiu (Author)
Publisher:Packt Publishing - ebooks Account
Publication Date: 11 Jun. 2024
Language:English
Print Length:379 pages
ISBN-10:183508950X
ISBN-13:9781835089507
Book Description
Elegantly solve real-world problems with AI, using the LlamaIndex data framework to enhance your LLM-based Python applications
Key FeaturesExamine text chunking effects on RAG workflows and understand security in RAG app development.Discover chatbots and agents and learn how to build complex conversation enginesBuild as you learn by applying fresh knowledge with a useful hands-on project
Book DescriptionMany enthusiasts, as well as more experienced programmers, have already discovered the immense potential that Generative AI, such as Large Language Models, possess. These models simplify problems but have limitations, including contextual memory constraints, prompt size issues, real-time data gaps, and occasional "hallucinations."With this book you will be taken through all the necessary steps: from preparing the environment to gradually adding features and deploying the final project. Starting from fundamental LLM concepts to exploring the features of this framework. Practical examples guide you through necessary steps on personalising and launching your LlamaIndex projects. Overcome LLM limitations, build end-user applications, and acquire skills in ingesting, indexing, querying, and connecting dynamic knowledge bases. The book covers Generative AI and LLM understanding, LlamaIndex deployment, and concludes with customisation, providing a holistic grasp of LlamaIndex's capabilities and applications.By the end of the book, you will be able to resolve challenges in LLMs and build interactive AI-driven applications by applying best practices in prompt engineering and troubleshooting Generative AI projects.
What you will learnUnderstand the LlamaIndex ecosystem and common use casesMaster techniques to ingest and parse data from various sources into LlamaIndexDiscover how to create optimized indexes tailored to your use casesLearn to query LlamaIndex effectively and interpret responsesBuild an end-to-end interactive web application with LlamaIndex, Python and StreamlitCustomize LlamaIndex configuration based on your project needsPredict costs and deal with potential privacy issuesDeploy LlamaIndex applications for others to utilize
Who this book is forThis book is ideal for Python developers with basic knowledge of NLP and LLMs aiming to build interactive LLM applications. Experienced developers and conversational AI developers will benefit from advanced techniques to fully unleash the capabilities of the framework.
Table of contentsUnderstanding Large Language ModelsLlamaIndex: The Hidden JewelKickstarting your journey with LlamaIndexData ingestion with LlamaIndexIndexing with LlamaIndexQuerying your data Part 1 - Context RetrievalQuerying our data - Part 2 – Post-processing and response synthesisBuilding chatbots and agents with LlamaIndexCustomizing and deploying our LlamaIndex projectPrompt engineering guidelines and best practiceFinal conclusion and additional resources
About the Author
Elegantly solve real-world problems with AI, using the LlamaIndex data framework to enhance your LLM-based Python applications
Key FeaturesExamine text chunking effects on RAG workflows and understand security in RAG app development.Discover chatbots and agents and learn how to build complex conversation enginesBuild as you learn by applying fresh knowledge with a useful hands-on project
Book DescriptionMany enthusiasts, as well as more experienced programmers, have already discovered the immense potential that Generative AI, such as Large Language Models, possess. These models simplify problems but have limitations, including contextual memory constraints, prompt size issues, real-time data gaps, and occasional "hallucinations."With this book you will be taken through all the necessary steps: from preparing the environment to gradually adding features and deploying the final project. Starting from fundamental LLM concepts to exploring the features of this framework. Practical examples guide you through necessary steps on personalising and launching your LlamaIndex projects. Overcome LLM limitations, build end-user applications, and acquire skills in ingesting, indexing, querying, and connecting dynamic knowledge bases. The book covers Generative AI and LLM understanding, LlamaIndex deployment, and concludes with customisation, providing a holistic grasp of LlamaIndex's capabilities and applications.By the end of the book, you will be able to resolve challenges in LLMs and build interactive AI-driven applications by applying best practices in prompt engineering and troubleshooting Generative AI projects.
What you will learnUnderstand the LlamaIndex ecosystem and common use casesMaster techniques to ingest and parse data from various sources into LlamaIndexDiscover how to create optimized indexes tailored to your use casesLearn to query LlamaIndex effectively and interpret responsesBuild an end-to-end interactive web application with LlamaIndex, Python and StreamlitCustomize LlamaIndex configuration based on your project needsPredict costs and deal with potential privacy issuesDeploy LlamaIndex applications for others to utilize
Who this book is forThis book is ideal for Python developers with basic knowledge of NLP and LLMs aiming to build interactive LLM applications. Experienced developers and conversational AI developers will benefit from advanced techniques to fully unleash the capabilities of the framework.
Table of contentsUnderstanding Large Language ModelsLlamaIndex: The Hidden JewelKickstarting your journey with LlamaIndexData ingestion with LlamaIndexIndexing with LlamaIndexQuerying your data Part 1 - Context RetrievalQuerying our data - Part 2 – Post-processing and response synthesisBuilding chatbots and agents with LlamaIndexCustomizing and deploying our LlamaIndex projectPrompt engineering guidelines and best practiceFinal conclusion and additional resources
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