Unlocking Data with Generative AI and RAG: Enhance generative AI systems by integrating internal data with large language models using RAG (利用生成式人工智慧與RAG解鎖數據：透過RAG將內部數據與大型語言模型整合，提升生成式人工智慧系統)

Bourne, Keith, Es, Shahul

Unlocking Data with Generative AI and RAG: Enhance generative AI systems by integrating internal data with large language models using RAG
  • 出版商: Packt Publishing
  • 出版日期: 2024-09-27
  • 售價: $1,680
  • 貴賓價: 9.5 折 $1,596
  • 語言: 英文
  • 頁數: 346
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1835887902
  • ISBN-13: 9781835887905
  • 相關分類: LangChain、人工智慧

    • 海外代購書籍(需單獨結帳)

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商品描述

Leverage cutting-edge generative AI techniques such as RAG to realize the potential of your data and drive innovation as well as gain strategic advantage

Key Features:

- Optimize data retrieval and generation using vector databases

- Boost decision-making and automate workflows with AI agents

- Overcome common challenges in implementing real-world RAG systems

- Purchase of the print or Kindle book includes a free PDF eBook

Book Description:

Generative AI is helping organizations tap into their data in new ways, with retrieval-augmented generation (RAG) combining the strengths of large language models (LLMs) with internal data for more intelligent and relevant AI applications. The author harnesses his decade of ML experience in this book to equip you with the strategic insights and technical expertise needed when using RAG to drive transformative outcomes.

The book explores RAG's role in enhancing organizational operations by blending theoretical foundations with practical techniques. You'll work with detailed coding examples using tools such as LangChain and Chroma's vector database to gain hands-on experience in integrating RAG into AI systems. The chapters contain real-world case studies and sample applications that highlight RAG's diverse use cases, from search engines to chatbots. You'll learn proven methods for managing vector databases, optimizing data retrieval, effective prompt engineering, and quantitatively evaluating performance. The book also takes you through advanced integrations of RAG with cutting-edge AI agents and emerging non-LLM technologies.

By the end of this book, you'll be able to successfully deploy RAG in business settings, address common challenges, and push the boundaries of what's possible with this revolutionary AI technique.

What You Will Learn:

- Understand RAG principles and their significance in generative AI

- Integrate LLMs with internal data for enhanced operations

- Master vectorization, vector databases, and vector search techniques

- Develop skills in prompt engineering specific to RAG and design for precise AI responses

- Familiarize yourself with AI agents' roles in facilitating sophisticated RAG applications

- Overcome scalability, data quality, and integration issues

- Discover strategies for optimizing data retrieval and AI interpretability

Who this book is for:

This book is for AI researchers, data scientists, software developers, and business analysts looking to leverage RAG and generative AI to enhance data retrieval, improve AI accuracy, and drive innovation. It is particularly suited for anyone with a foundational understanding of AI who seeks practical, hands-on learning. The book offers real-world coding examples and strategies for implementing RAG effectively, making it accessible to both technical and non-technical audiences. A basic understanding of Python and Jupyter Notebooks is required.

Table of Contents

- What Is Retrieval-Augmented Generation (RAG)

- Code Lab - An Entire RAG Pipeline

- Practical Applications of RAG

- Components of a RAG System

- Managing Security in RAG Applications

- Interfacing with RAG and Gradio

- The Key Role Vectors and Vector Stores Play in RAG

- Similarity Searching with Vectors

- Evaluating RAG Quantitatively and with Visualizations

- Key RAG Components in LangChain

- Using LangChain to Get More from RAG

- Combining RAG with the Power of AI Agents and LangGraph

- Using Prompt Engineering to Improve RAG Efforts

- Advanced RAG-Related Techniques for Improving Results

商品描述(中文翻譯)

利用尖端的生成式 AI 技術，如 RAG，來實現數據的潛力，推動創新並獲得戰略優勢

主要特點：
- 使用向量數據庫優化數據檢索和生成
- 通過 AI 代理提升決策制定和自動化工作流程
- 克服實施現實世界 RAG 系統的常見挑戰
- 購買印刷版或 Kindle 書籍可獲得免費 PDF 電子書

書籍描述：
生成式 AI 正在幫助組織以新的方式利用其數據，檢索增強生成（RAG）結合了大型語言模型（LLMs）與內部數據的優勢，為更智能和相關的 AI 應用提供支持。作者在本書中運用其十年的機器學習經驗，為您提供使用 RAG 推動變革性成果所需的戰略洞察和技術專業知識。

本書探討 RAG 在提升組織運營中的角色，將理論基礎與實用技術相結合。您將使用 LangChain 和 Chroma 的向量數據庫等工具進行詳細的編碼示例，以獲得將 RAG 整合到 AI 系統中的實踐經驗。各章節包含現實世界的案例研究和示範應用，突顯 RAG 的多樣化用例，從搜索引擎到聊天機器人。您將學習管理向量數據庫、優化數據檢索、有效的提示工程和定量評估性能的有效方法。本書還將帶您了解 RAG 與尖端 AI 代理及新興非 LLM 技術的高級整合。

在本書結束時，您將能夠在商業環境中成功部署 RAG，解決常見挑戰，並推動這一革命性 AI 技術的可能性邊界。

您將學到的內容：
- 理解 RAG 原則及其在生成式 AI 中的重要性
- 將 LLM 與內部數據整合以增強運營
- 精通向量化、向量數據庫和向量搜索技術
- 發展針對 RAG 的提示工程技能，設計精確的 AI 回應
- 熟悉 AI 代理在促進複雜 RAG 應用中的角色
- 克服可擴展性、數據質量和整合問題
- 發現優化數據檢索和 AI 可解釋性的策略

本書適合對象：
本書適合 AI 研究人員、數據科學家、軟體開發人員和商業分析師，旨在利用 RAG 和生成式 AI 來增強數據檢索、提高 AI 準確性並推動創新。特別適合對 AI 有基礎理解並尋求實踐、動手學習的人士。本書提供現實世界的編碼示例和有效實施 RAG 的策略，使其對技術和非技術讀者均可接觸。需要具備基本的 Python 和 Jupyter Notebooks 知識。

目錄：
- 什麼是檢索增強生成（RAG）
- 編碼實驗室 - 整個 RAG 流程
- RAG 的實用應用
- RAG 系統的組成部分
- 管理 RAG 應用中的安全性
- 與 RAG 和 Gradio 的介面
- 向量和向量存儲在 RAG 中的關鍵角色
- 使用向量進行相似性搜索
- 定量和可視化評估 RAG
- LangChain 中的關鍵 RAG 組件
- 使用 LangChain 從 RAG 中獲得更多
- 將 RAG 與 AI 代理和 LangGraph 的力量結合
- 使用提示工程改善 RAG 效果
- 改善結果的高級 RAG 相關技術

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