Building Generative AI Agents: Using Langgraph, Autogen, and Crewai
暫譯: 構建生成式 AI 代理：使用 Langgraph、Autogen 和 Crewai
Taulli, Tom, Deshmukh, Gaurav
相關主題
商品描述
The dawn of AI agents is upon us. Tech visionaries like Bill Gates, Andrew Ng, and Vinod Khosla have highlighted the monumental potential of this powerful technology. This book will provide the knowledge and tools necessary to build generative AI agents using the most popular frameworks, such as AutoGen, LangChain, LangGraph, CrewAI, and Haystack.
Recent breakthroughs in large language models have opened up unprecedented possibilities. After years of gradual progress in machine learning and deep learning, we are now witnessing novel approaches capable of understanding, reasoning, and generating content in ways that promise to revolutionize nearly every industry. This platform shift is as significant as the advent of mainframes, PCs, cloud computing, mobile technology, and social media. It's why the world's largest technology companies - like Microsoft, Apple, Google, and Meta - are making enormous investments in this category.
While chatbots like ChatGPT, Claude, and Gemini have demonstrated remarkable potential, the years ahead will see the rise of generative AI agents capable of executing complex tasks on behalf of users. These agents already exhibit capabilities such as running test suites, searching the web for documentation, writing software, answering questions based on vast organized information, and performing intricate web-based tasks across multiple domains. They can autonomously investigate cybersecurity incidents and address complex customer support needs. By integrating skills, knowledge bases, planning frameworks, memory, and feedback loops, these systems can handle many tasks and improve over time.
Building Generative AI Agents serves as a high-quality guide for developers to understand when and where AI agents can be useful, their advantages and disadvantages, and practical advice on designing, building, deploying, and monitoring them.
What You Will Learn
- The foundational concepts, capabilities, and potential of AI agents.
- Recent innovations in large language models that have enabled the development of AI agents.
- How to build AI agents for launching a product, creating a financial plan, handling customer service, and using Retrieval Augmented Generation (RAG).
- Essential frameworks for building generative AI agents, including AutoGen, LangChain, LangGraph, CrewAI, and Haystack.
- Step-by-step guidance on designing, building, and deploying AI agents.
- Insights into the future of AI agents and their potential impact on various industries.
Who This Book Is For
Experienced software developers
商品描述(中文翻譯)
AI 代理的曙光已經來臨。像比爾·蓋茲（Bill Gates）、吳恩達（Andrew Ng）和維諾德·科斯拉（Vinod Khosla）這樣的科技先驅已經強調了這項強大技術的巨大潛力。本書將提供必要的知識和工具，以使用最受歡迎的框架（如 AutoGen、LangChain、LangGraph、CrewAI 和 Haystack）來構建生成式 AI 代理。
最近在大型語言模型方面的突破開啟了前所未有的可能性。在機器學習和深度學習逐步進展的多年後，我們現在目睹了能夠理解、推理和生成內容的新方法，這些方法有望徹底改變幾乎每個行業。這一平台的轉變與大型主機、個人電腦、雲計算、移動技術和社交媒體的出現一樣重要。這就是為什麼全球最大的科技公司——如微軟（Microsoft）、蘋果（Apple）、谷歌（Google）和Meta——在這一領域進行巨額投資的原因。
雖然像 ChatGPT、Claude 和 Gemini 這樣的聊天機器人已經展示了驚人的潛力，但未來幾年將會出現能夠代表用戶執行複雜任務的生成式 AI 代理。這些代理已經展現出如運行測試套件、在網路上搜尋文檔、編寫軟體、根據大量組織化信息回答問題，以及在多個領域執行複雜的網路任務等能力。它們可以自主調查網絡安全事件並解決複雜的客戶支持需求。通過整合技能、知識庫、規劃框架、記憶和反饋循環，這些系統能夠處理許多任務並隨著時間的推移不斷改進。
《構建生成式 AI 代理》作為一個高品質的指南，幫助開發者了解 AI 代理何時何地可以發揮作用、它們的優缺點，以及在設計、構建、部署和監控它們方面的實用建議。
您將學到的內容：
- AI 代理的基本概念、能力和潛力。
- 促成 AI 代理開發的最新大型語言模型創新。
- 如何為產品推出、創建財務計劃、處理客戶服務以及使用檢索增強生成（Retrieval Augmented Generation, RAG）來構建 AI 代理。
- 構建生成式 AI 代理的基本框架，包括 AutoGen、LangChain、LangGraph、CrewAI 和 Haystack。
- 設計、構建和部署 AI 代理的逐步指導。
- 對 AI 代理未來及其對各行業潛在影響的見解。
本書適合對象：
經驗豐富的軟體開發者。
作者簡介
Tom Taulli (@ttaulli) is a consultant to various companies, such as Aisera, a venture-backed generative AI startup. He has written several books like AI Basics and Generative AI. Tom has also taught IT courses for UCLA, PluralSight and O'Reilly Media. For these, he has provided lessons in using Python to create deep learning and machine learning models. He has also taught on topics like NLP (Natural Language Processing).
Gaurav Deshmukh is a highly skilled technology leader with over a decade of experience driving transformative software engineering initiatives. Throughout his career, he has held pivotal technical roles at prominent companies such as Guidewire, Cigna, Home Depot, American Agricultural Laboratory (AmAgLab), Tata Exlsi, and Amdocs. Gaurav's expertise encompasses a range of cutting-edge technologies, including cloud computing, cybersecurity, software automation, data engineering, and full-stack development with various programming languages and web technology frameworks. He employs his vast knowledge to create innovative solutions that optimize workflows and drive business growth. Gaurav holds both an MBA and a Master's degree in Computer Science, with a focus on data warehousing and computer vision. He is dedicated to elevating the strategic role of software engineering in delivering business value. As a distinguished leader, Gaurav can be reached at gauravkdeshmukh89@gmail.com to explore transformative technical initiatives.
作者簡介(中文翻譯)
Tom Taulli (@ttaulli) 是多家公司的顧問，例如 Aisera，一家獲得風險投資的生成式 AI 初創公司。他撰寫了幾本書，如《AI Basics》和《Generative AI》。Tom 也曾在 UCLA、PluralSight 和 O'Reilly Media 教授 IT 課程。在這些課程中，他提供了使用 Python 創建深度學習和機器學習模型的教學。他還教授過自然語言處理 (NLP) 等主題。
Gaurav Deshmukh 是一位技術領袖，擁有超過十年的經驗，推動變革性的軟體工程計畫。在他的職業生涯中，他在多家知名公司擔任關鍵技術角色，如 Guidewire、Cigna、Home Depot、美國農業實驗室 (AmAgLab)、Tata Exlsi 和 Amdocs。Gaurav 的專業知識涵蓋一系列尖端技術，包括雲計算、網路安全、軟體自動化、數據工程以及使用各種程式語言和網頁技術框架的全端開發。他利用其豐富的知識創造創新解決方案，以優化工作流程並推動業務增長。Gaurav 擁有 MBA 和計算機科學碩士學位，專注於數據倉儲和計算機視覺。他致力於提升軟體工程在提供商業價值中的戰略角色。作為一位傑出的領導者，Gaurav 可以通過電子郵件 gauravkdeshmukh89@gmail.com 聯繫，以探討變革性的技術計畫。
