Building Data-Driven Applications with LlamaIndex: A practical guide to retrieval-augmented generation (RAG) to enhance LLM applications
Gheorghiu, Andrei
- 出版商: Packt Publishing
- 出版日期: 2024-05-10
- 售價: $1,800
- 貴賓價: 9.5 折 $1,710
- 語言: 英文
- 頁數: 368
- 裝訂: Quality Paper - also called trade paper
- ISBN: 183508950X
- ISBN-13: 9781835089507
-
相關分類:
LangChain
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相關主題
商品描述
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
Discover the immense potential of Generative AI and Large Language Models (LLMs) with this comprehensive guide. Learn to overcome LLM limitations, such as contextual memory constraints, prompt size issues, real-time data gaps, and occasional 'hallucinations'. Follow practical examples to personalize and launch your LlamaIndex projects, mastering skills in ingesting, indexing, querying, and connecting dynamic knowledge bases. From fundamental LLM concepts to LlamaIndex deployment and customization, this book provides a holistic grasp of LlamaIndex's capabilities and applications. By the end, you'll be able to resolve LLM challenges and build interactive AI-driven applications using best practices in prompt engineering and troubleshooting Generative AI projects.olve 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
商品描述(中文翻譯)
解決現實世界的問題,輕鬆運用人工智慧 (AI) 透過 LlamaIndex 數據框架來增強您的基於 LLM 的 Python 應用程式
主要特點
- 檢視文本分塊對 RAG 工作流程的影響,並了解 RAG 應用開發中的安全性
- 探索聊天機器人和代理,學習如何構建複雜的對話引擎
- 在學習的同時進行實作,將所學知識應用於實際專案
書籍描述
透過這本全面的指南,發現生成式 AI 和大型語言模型 (LLMs) 的巨大潛力。學習克服 LLM 的限制,例如上下文記憶限制、提示大小問題、即時數據缺口以及偶爾的「幻覺」。跟隨實用範例,個性化並啟動您的 LlamaIndex 專案,掌握攝取、索引、查詢和連接動態知識庫的技能。從基本的 LLM 概念到 LlamaIndex 的部署和自訂,本書提供了對 LlamaIndex 能力和應用的全面理解。到最後,您將能夠解決 LLM 的挑戰,並運用最佳實踐在提示工程和生成式 AI 專案的故障排除中構建互動式 AI 驅動的應用程式。
您將學到的內容
- 了解 LlamaIndex 生態系統和常見用例
- 掌握從各種來源攝取和解析數據到 LlamaIndex 的技術
- 探索如何創建針對您的用例優化的索引
- 了解如何有效查詢 LlamaIndex 並解釋回應
- 使用 LlamaIndex、Python 和 Streamlit 構建端到端的互動式網頁應用程式
- 根據您的專案需求自訂 LlamaIndex 配置
- 預測成本並處理潛在的隱私問題
- 部署其他人可以使用的 LlamaIndex 應用程式
本書適合對象
本書適合具備自然語言處理 (NLP) 和 LLM 基礎知識的 Python 開發者,尋求構建互動式 LLM 應用程式。經驗豐富的開發者和對話式 AI 開發者也將從書中涵蓋的進階技術中受益,以充分發揮該框架的能力。
目錄
- 了解大型語言模型
- LlamaIndex:隱藏的瑰寶 - LlamaIndex 生態系統介紹
- 開啟您的 LlamaIndex 之旅
- 將數據攝取到我們的 RAG 工作流程
- 使用 LlamaIndex 進行索引
- 查詢我們的數據,第 1 部分 - 上下文檢索
- 查詢我們的數據,第 2 部分 - 後處理和回應合成
- 使用 LlamaIndex 構建聊天機器人和代理
- 自訂和部署我們的 LlamaIndex 專案
- 提示工程指導方針和最佳實踐
- 結論與附加資源