Building Data-Driven Applications with LlamaIndex: A practical guide to retrieval-augmented generation (RAG) to enhance LLM applications
Synopsis "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 FeaturesExamine text chunking effects on RAG workflows and understand security in RAG app developmentDiscover chatbots and agents and learn how to build complex conversation enginesBuild as you learn by applying the knowledge you gain to a hands-on projectBook DescriptionGenerative AI, such as Large Language Models (LLMs) possess immense potential. 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'll go from preparing the environment to gradually adding features and deploying the final project. You'll gradually progress from fundamental LLM concepts to exploring the features of this framework. Practical examples will guide you through essential steps for personalizing and launching your LlamaIndex projects. Additionally, you'll overcome LLM limitations, build end-user applications, and acquire skills in ingesting, indexing, querying, and connecting dynamic knowledge bases, covering Generative AI and LLM, as well as LlamaIndex deployment. As you approach the conclusion, you'll delve into customization, gaining a holistic grasp of LlamaIndex's capabilities and applications.By the end of the book, you'll 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 casesUnderstand how to query LlamaIndex effectively and interpret responsesBuild an end-to-end interactive web application with LlamaIndex, Python, and StreamlitCustomize a LlamaIndex configuration based on your project needsPredict costs and deal with potential privacy issuesDeploy LlamaIndex applications that others can useWho this book is forThis book is for Python developers with basic knowledge of natural language processing (NLP) and LLMs looking to build interactive LLM applications. Experienced developers and conversational AI developers will also benefit from the advanced techniques covered in the book to fully unleash the capabilities of the framework.Table of ContentsUnderstanding Large Language ModelsLlamaIndex: The Hidden Jewel - An Introduction to the LlamaIndex EcosystemKickstarting Your Journey with LlamaIndexIngesting Data into Our RAG WorkflowIndexing with LlamaIndexQuerying Our Data, Part 1 - Context RetrievalQuerying Our Data, Part 2 - Postprocessing and Response SynthesisBuilding Chatbots and Agents with LlamaIndexCustomizing and Deploying Our LlamaIndex ProjectPrompt Engineering Guidelines and Best PracticesConclusions and Additional Resources
