Generative AI Apps with Langchain and Python: A Project-Based Approach to Building Real-World LLM Apps
暫譯: 使用 Langchain 和 Python 的生成式 AI 應用程式：基於專案的實作真實世界 LLM 應用程式方法

Jay, Rabi

Generative AI Apps with Langchain and Python: A Project-Based Approach to Building Real-World LLM Apps
  • 出版商: Apress
  • 出版日期: 2024-12-27
  • 售價: $2,350
  • 貴賓價: 9.5 折 $2,232
  • 語言: 英文
  • 頁數: 513
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 9798868808814
  • ISBN-13: 9798868808814
  • 相關分類: LangChain、Python

    • 立即出貨 (庫存=1)

LLM 大語言模型｜2書75折 詳見活動內容 »

商品描述

Future-proof your programming career through practical projects designed to grasp the intricacies of LangChain's components, from core chains to advanced conversational agents. This hands-on book provides Python developers with the necessary skills to develop real-world Large Language Model (LLM)-based Generative AI applications quickly, regardless of their experience level.

Projects throughout the book offer practical LLM solutions for common business issues, such as information overload, internal knowledge access, and enhanced customer communication. Meanwhile, you'll learn how to optimize workflows, enhance embedding efficiency, select between vector stores, and other optimizations relevant to experienced AI users. The emphasis on real-world applications and practical examples will enable you to customize your own projects to address pain points across various industries.

Developing LangChain-based Generative AI LLM Apps with Python employs a focused toolkit (LangChain, Pinecone, and Streamlit LLM integration) to practically showcase how Python developers can leverage existing skills to build Generative AI solutions. By addressing tangible challenges, you'll learn-by-be doing, enhancing your career possibilities in today's rapidly evolving landscape.

What You Will Learn

  • Understand different types of LLMs and how to select the right ones for responsible AI.
  • Structure effective prompts.
  • Master LangChain concepts, such as chains, models, memory, and agents.
  • Apply embeddings effectively for search, content comparison, and understanding similarity.
  • Setup and integrate Pinecone vector database for indexing, structuring data, and search.
  • Build Q & A applications for multiple doc formats.
  • Develop multi-step AI workflow apps using LangChain agents.

Who This Book Is For

Python programmers who aim to develop a basic understanding of AI concepts and move from LLM theory to practical Generative AI application development using LangChain; those seeking a structured guide to enhance their careers by learning to create robust, real-world LLM-powered Generative AI applications; data scientists, analysts, and experienced developers new to LLMs.

商品描述(中文翻譯)

未來證明您的程式設計職業生涯，透過實用專案來掌握 LangChain 的組件細節，從核心鏈接到進階對話代理。這本實作導向的書籍為 Python 開發者提供必要的技能，以快速開發基於大型語言模型（Large Language Model, LLM）的生成式 AI 應用，無論其經驗水平如何。

書中的專案提供針對常見商業問題的實用 LLM 解決方案，例如資訊過載、內部知識存取和增強客戶溝通。同時，您將學習如何優化工作流程、提升嵌入效率、在向量儲存之間進行選擇，以及其他與經驗豐富的 AI 使用者相關的優化。強調實際應用和實例將使您能夠自訂自己的專案，以解決各行各業的痛點。

《使用 Python 開發基於 LangChain 的生成式 AI LLM 應用》採用專注的工具包（LangChain、Pinecone 和 Streamlit LLM 整合），實際展示 Python 開發者如何利用現有技能來構建生成式 AI 解決方案。通過解決具體挑戰，您將在實踐中學習，提升您在當今快速變化的環境中的職業可能性。

您將學到的內容：
- 理解不同類型的 LLM 以及如何選擇適合負責任 AI 的 LLM。
- 結構有效的提示。
- 精通 LangChain 概念，例如鏈接、模型、記憶和代理。
- 有效應用嵌入進行搜尋、內容比較和理解相似性。
- 設置和整合 Pinecone 向量資料庫以進行索引、結構化數據和搜尋。
- 為多種文檔格式構建問答應用。
- 使用 LangChain 代理開發多步驟的 AI 工作流程應用。

本書適合對象：
希望發展 AI 概念基本理解的 Python 程式設計師，並從 LLM 理論轉向使用 LangChain 開發實用的生成式 AI 應用；尋求結構化指南以透過學習創建穩健的、實際的 LLM 驅動生成式 AI 應用來提升職業生涯的人；以及對 LLM 新手的數據科學家、分析師和經驗豐富的開發者。

作者簡介

Rabi Jay has over 15 years of experience driving digital transformation with a unique blend of technical depth and business acumen. His background as a Java and SAP ABAP developer provides insights into the enterprise systems LLMs often needed to integrate with. As a leader in Deloitte's Digital / Cloud Native practice, he has gained cross-industry experience applying AI solutions, positioning him to identify where LLMs offer the greatest potential for business impact.

He is passionate about making complex technology accessible, leading him to authoring books on SAP NetWeaver Portal Technology and "Enterprise AI in the Cloud" along with regular contributions to industry publications. His role as a technical reviewer for Large Language Model Based Solutions, Modern Python Development Using ChatGPT, and as Vice President at HCL America, focused on digital transformation, demonstrate his active engagement in the LLM field. Additionally, he runs a LinkedIn newsletter ("Enterprise AI Transformation") and free LinkedIn course ("Generative AI for Business Innovation").

作者簡介(中文翻譯)

Rabi Jay 擁有超過 15 年的數位轉型經驗，結合了深厚的技術背景和商業敏銳度。他作為 Java 和 SAP ABAP 開發者的背景，使他能夠深入了解企業系統，這些系統通常需要與大型語言模型 (LLMs) 整合。作為德勤 (Deloitte) 數位/雲原生實務的領導者，他在應用 AI 解決方案方面獲得了跨行業的經驗，使他能夠識別 LLMs 在商業影響方面的最大潛力。

他熱衷於使複雜的技術變得易於接觸，這促使他撰寫有關 SAP NetWeaver Portal Technology 和《雲端中的企業 AI》的書籍，並定期為行業出版物貢獻文章。他作為《基於大型語言模型的解決方案》、《使用 ChatGPT 的現代 Python 開發》的技術審查員，以及在 HCL America 擔任副總裁，專注於數位轉型，顯示了他在 LLM 領域的積極參與。此外，他還運營一個 LinkedIn 通訊（《企業 AI 轉型》）和免費的 LinkedIn 課程（《用於商業創新的生成式 AI》）。

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