Building AI Intensive Python Applications: Create intelligent apps with LLMs and vector databases
Palmer, Rachelle, Perlmutter, Ben, Gangadhar, Ashwin
相關主題
商品描述
Master retrieval-augmented generation architecture and fine-tune your AI stack, along with discovering real-world use cases and best practices to create powerful AI apps
Key Features:
- Get to grips with the fundamentals of LLMs, vector databases, and Python frameworks
- Implement effective retrieval-augmented generation strategies with MongoDB Atlas
- Optimize AI models for performance and accuracy with model compression and deployment optimization
- Purchase of the print or Kindle book includes a free PDF eBook
Book Description:
The era of generative AI is upon us, and this book serves as a roadmap to harness its full potential. With its help, you'll learn the core components of the AI stack: large language models (LLMs), vector databases, and Python frameworks, and see how these technologies work together to create intelligent applications.
The chapters will help you discover best practices for data preparation, model selection, and fine-tuning, and teach you advanced techniques such as retrieval-augmented generation (RAG) to overcome common challenges, such as hallucinations and data leakage. You'll get a solid understanding of vector databases, implement effective vector search strategies, refine models for accuracy, and optimize performance to achieve impactful results. You'll also identify and address AI failures to ensure your applications deliver reliable and valuable results. By evaluating and improving the output of LLMs, you'll be able to enhance their performance and relevance.
By the end of this book, you'll be well-equipped to build sophisticated AI applications that deliver real-world value.
What You Will Learn:
- Understand the architecture and components of the generative AI stack
- Explore the role of vector databases in enhancing AI applications
- Master Python frameworks for AI development
- Implement Vector Search in AI applications
- Find out how to effectively evaluate LLM output
- Overcome common failures and challenges in AI development
Who this book is for:
This book is for software engineers and developers looking to build intelligent applications using generative AI. While the book is suitable for beginners, a basic understanding of Python programming is required to make the most of it.
Table of Contents
- Getting Started with Generative AI
- Building Blocks of Intelligent Applications
- Large Language Models
- Embedding Models
- Vector Databases
- AI/ML Application Design
- Useful Frameworks, Libraries, and APIs
- Implementing Vector Search in AI Applications
- LLM Output Evaluation
- Refining the Semantic Data Model to Improve Accuracy
- Common Failures of Generative AI
- Correcting and Optimizing Your Generative AI Application
商品描述(中文翻譯)
**掌握檢索增強生成架構，微調您的 AI 技術堆疊，並探索實際應用案例和最佳實踐，以創建強大的 AI 應用程式**
**主要特點：**
- 理解大型語言模型（LLMs）、向量資料庫和 Python 框架的基本概念
- 使用 MongoDB Atlas 實施有效的檢索增強生成策略
- 通過模型壓縮和部署優化來優化 AI 模型的性能和準確性
- 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 的時代已經來臨，本書作為一個路線圖，幫助您充分利用其潛力。在本書的幫助下，您將學習 AI 技術堆疊的核心組件：大型語言模型（LLMs）、向量資料庫和 Python 框架，並了解這些技術如何協同工作以創建智能應用程式。
各章節將幫助您發現數據準備、模型選擇和微調的最佳實踐，並教授您檢索增強生成（RAG）等高級技術，以克服常見挑戰，如幻覺和數據洩漏。您將深入了解向量資料庫，實施有效的向量搜索策略，精煉模型以提高準確性，並優化性能以實現有影響力的結果。您還將識別並解決 AI 失敗，以確保您的應用程式提供可靠且有價值的結果。通過評估和改進 LLM 的輸出，您將能夠提升其性能和相關性。
在本書結束時，您將具備構建提供實際價值的複雜 AI 應用程式的能力。
**您將學到的內容：**
- 理解生成式 AI 技術堆疊的架構和組件
- 探索向量資料庫在增強 AI 應用程式中的角色
- 精通 AI 開發的 Python 框架
- 在 AI 應用程式中實施向量搜索
- 瞭解如何有效評估 LLM 輸出
- 克服 AI 開發中的常見失敗和挑戰
**本書適合誰：**
本書適合希望使用生成式 AI 構建智能應用程式的軟體工程師和開發人員。雖然本書適合初學者，但需要具備基本的 Python 程式設計知識，以便充分利用本書內容。
**目錄：**
- 生成式 AI 入門
- 智能應用程式的構建基石
- 大型語言模型
- 嵌入模型
- 向量資料庫
- AI/ML 應用設計
- 有用的框架、庫和 API
- 在 AI 應用程式中實施向量搜索
- LLM 輸出評估
- 精煉語義數據模型以提高準確性
- 生成式 AI 的常見失敗
- 修正和優化您的生成式 AI 應用程式