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Building Data-Driven Applications with LlamaIndex A practical guide to retrieval-augmented generation (RAG) to enhance LLM [#956172]
Free Download Building Data-Driven Applications with LlamaIndex: A practical guide to retrieval-augmented generation (RAG) to enhance LLM applications
English | May 10, 2024 | ASIN: B0CWDTLJ5G | 620 pages | EPUB (True) | 13.22 MB
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 how to build complex conversation engines
Build as you learn by applying the knowledge you gain to a hands-on project
Book Description
Generative 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 learn
Understand the LlamaIndex ecosystem and common use cases
Master techniques to ingest and parse data from various sources into LlamaIndex
Discover how to create optimized indexes tailored to your use cases
Understand how to query LlamaIndex effectively and interpret responses
Build an end-to-end interactive web application with LlamaIndex, Python, and Streamlit
Customize a LlamaIndex configuration based on your project needs
Predict costs and deal with potential privacy issues
Deploy LlamaIndex applications that others can use
Who this book is for
This 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 Contents
Understanding Large Language Models
LlamaIndex: The Hidden Jewel - An Introduction to the LlamaIndex Ecosystem
Kickstarting Your Journey with LlamaIndex
Ingesting Data into Our RAG Workflow
Indexing with LlamaIndex
Querying Our Data, Part 1 - Context Retrieval
Querying Our Data, Part 2 - Postprocessing and Response Synthesis
Building Chatbots and Agents with LlamaIndex
Customizing and Deploying Our LlamaIndex Project
Prompt Engineering Guidelines and Best Practices
Conclusions and Additional Resources
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