商品描述
Harness the power of Large Language Models (LLMs) to build cutting-edge AI applications with Python and LangChain. This book provides a hands-on approach to understanding, implementing, and deploying LLM-powered solutions, equipping developers, data scientists, and AI enthusiasts with the tools to create real-world AI applications. The journey begins with an introduction to LangChain, covering its core concepts, integration with Python, and essential components such as prompt engineering, memory management, and retrieval-augmented generation (RAG). As you progress, you'll explore advanced AI workflows, including multi-agent architectures, fine-tuning strategies, and optimization techniques to maximize LLM efficiency. The book also takes a deep dive into practical applications of LLMs, guiding you through the development of intelligent chatbots, document retrieval systems, content generation pipelines, and AI-driven automation tools. You'll learn how to leverage APIs, integrate LLMs into web and mobile platforms, and optimize large-scale deployments while addressing key challenges such as inference latency, cost efficiency, and ethical considerations. By the end of the book, you'll have gained a solid understanding of LLM architectures, hands-on experience with LangChain, and the expertise to build scalable AI applications that redefine human-computer interaction.
What You Will Learn - Understand the fundamentals of LangChain and Python for LLM development
Know advanced AI workflows, including fine-tuning and memory management Build AI-powered applications such as chatbots, retrieval systems, and automation tools Know deployment strategies and performance optimization for real-world use Use best practices for scalability, security, and responsible AI implementation Unlock the full potential of LLMs and take your AI development skills to the next level
Who This Book Is For
Software engineers and Python developers interested in learning the foundations of LLMs and building advanced modern LLM applications for various tasks
商品描述(中文翻譯)
利用大型語言模型（Large Language Models, LLMs）的力量，使用 Python 和 LangChain 建立尖端的 AI 應用程式。本書提供了一種實作導向的方法，幫助讀者理解、實現和部署 LLM 驅動的解決方案，為開發者、數據科學家和 AI 愛好者提供創建實際 AI 應用程式所需的工具。
這段旅程始於對 LangChain 的介紹，涵蓋其核心概念、與 Python 的整合，以及提示工程、記憶管理和檢索增強生成（Retrieval-Augmented Generation, RAG）等基本組件。隨著進度的推進，您將探索進階的 AI 工作流程，包括多代理架構、微調策略和優化技術，以最大化 LLM 的效率。
本書還深入探討 LLM 的實際應用，指導您開發智能聊天機器人、文檔檢索系統、內容生成管道和 AI 驅動的自動化工具。您將學習如何利用 API，將 LLM 整合到網頁和移動平台中，並在解決推理延遲、成本效率和倫理考量等關鍵挑戰的同時，優化大規模部署。
到本書結束時，您將對 LLM 架構有扎實的理解，擁有使用 LangChain 的實作經驗，並具備構建可擴展 AI 應用程式的專業知識，重新定義人機互動。
您將學到什麼
- 理解 LangChain 和 Python 在 LLM 開發中的基本原理
- 了解進階的 AI 工作流程，包括微調和記憶管理
- 構建 AI 驅動的應用程式，如聊天機器人、檢索系統和自動化工具
- 了解實際使用的部署策略和性能優化
- 使用最佳實踐以實現可擴展性、安全性和負責任的 AI 實施
- 釋放 LLM 的全部潛力，將您的 AI 開發技能提升到新水平
本書適合誰
對學習 LLM 基礎知識和為各種任務構建先進現代 LLM 應用程式感興趣的軟體工程師和 Python 開發者
作者簡介
Dilyan Grigorov is a software developer with a passion for Python software development, generative deep learning and machine learning, data structures, and algorithms. He is a Stanford student in the Graduate Program on Artificial Intelligence in the classes of people such as Andrew Ng, Fei-Fei Li, and Christopher Manning. He has been mentored by software engineers and AI experts from Google and Nvidia. Dilyan is an advocate for open source and the Python language itself. He has 16 years of industry experience programming in Python and has spent five of those years researching and testing Generative AI solutions. His passion stems from his background as an SEO specialist dealing with search engine algorithms daily. He enjoys engaging with the software community, often giving talks at local meetups and larger conferences. In his spare time, he enjoys reading books, hiking in the mountains, taking long walks, playing with his son, and playing the piano.
作者簡介(中文翻譯)
Dilyan Grigorov 是一位軟體開發人員，對 Python 軟體開發、生成式深度學習和機器學習、資料結構和演算法充滿熱情。他是史丹佛大學人工智慧研究所的研究生，與 Andrew Ng、Fei-Fei Li 和 Christopher Manning 等人同班。他曾受到來自 Google 和 Nvidia 的軟體工程師和 AI 專家的指導。 Dilyan 是開源和 Python 語言的倡導者。他在業界擁有 16 年的 Python 編程經驗，其中有五年專注於研究和測試生成式 AI 解決方案。 他的熱情源於他作為 SEO 專家，每天處理搜尋引擎演算法的背景。他喜歡與軟體社群互動，經常在當地的聚會和大型會議上發表演講。在空閒時間，他喜歡閱讀書籍、在山中健行、散步、和兒子玩耍以及彈鋼琴。
