{"product_id":"138410","title":"Practical LLM Application Development with LangChain and RAG ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/3jXPCf0swS3GqMiu0VX6f4XBIC8mSV.png?v=1765062971\" style=\"max-width:100%;max-height:10px\"\u003e\u003c\/div\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\n\n\u003cdiv style=\"width:95%\"\u003e\n\n\u003cdiv style=\"text-align:center;font-size:30px;font-weight:bolder;line-height:1.6em\"\u003e Practical LLM Application Development with LangChain and RAG \u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"border-bottom:1px;border-bottom-style:dotted;border-color:;padding-bottom:20px\"\u003e\u003ccenter\u003e\u003ctable align=\"center\" width=\"100%\"\u003e\u003ctbody style=\"border:0px\"\u003e\n\n\u003ctr\u003e\u003ctd align=\"center\" style=\"line-height:1.2em;text-align:center;font-size:18px;color:black;font-weight:bold;padding-bottom:20px;\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\u003ctr\u003e\u003ctd style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/image.yes24.com\/goods\/144417437\/XL\" style=\"max-width:100%;height:auto\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\u003c\/table\u003e\u003c\/center\u003e\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;{split_style6}padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e Description \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;word-break:break-all;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eBook Introduction\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\u003cdiv\u003e \u003cb\u003eLLM applications, now in practice!\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e As language models become more powerful, they present new challenges for users. \u003cbr\u003eHow do we connect external data and knowledge? How do we structure complex workflows? How do we control model illusions? This book answers these questions and guides us through solving real-world problems using LangChain and RAG technologies.\u003cbr\u003e\u003cbr\u003e This book systematically covers everything from the essential components of RankChain (chain, memory, tools, agents) to various RAG methodologies (retriever, reranker, hybrid search, multimodal processing, GraphRAG, ReAct patterns, and sLLM utilization).\u003cbr\u003e Each chapter begins with a conceptual explanation and progresses to increasingly complex exercises, teaching readers how to apply the basic code to their own projects.\u003cbr\u003e We've lowered the barrier to entry by going beyond simple API calls to understand the evolution of the RAG methodology and its application scenarios, and by providing examples that can be run directly in a colab environment. \u003cbr\u003eIn particular, it provides clear guidelines on which approach is appropriate for each situation and architectural patterns that can be extended to actual services.\u003cbr\u003e\n\u003c\/div\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cul\u003e\u003cli\u003e You can preview some of the book's contents.\u003cbr\u003e \u003cspan\u003ePreview\u003c\/span\u003e\n\n\u003c\/li\u003e\u003c\/ul\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eindex\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003e▣ Chapter 1: LangChain\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 1.1 Preparation for Langchain\u003cbr\u003e ___1.1.1 Why use chains?\u003cbr\u003e 1.2 Colab (Google Colaboratory) environment\u003cbr\u003e ___1.2.1 Setting up the Colab execution environment\u003cbr\u003e 1.3 LLM API Key\u003cbr\u003e ___1.3.1 GPT API Key\u003cbr\u003e ___1.3.2 Gemini API Key\u003cbr\u003e 1.4 Hugging Face\u003cbr\u003e ___1.4.1 Finding the Hugging Face Model\u003cbr\u003e 1.5 Langchain Components\u003cbr\u003e ___1.5.1 Chain\u003cbr\u003e ___1.5.2 Prompt\u003cbr\u003e ___1.5.3 Memory\u003cbr\u003e ___1.5.4 Index\u003cbr\u003e ___1.5.5 Callbacks and Evaluation\u003cbr\u003e\u003cbr\u003e \u003cb\u003e▣ Chapter 02: RAG (Retrieval-Augmented Generation)\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 2.1 Background and Importance of RAG\u003cbr\u003e 2.2 RAG using Langchain\u003cbr\u003e ___2.2.1 Preferences\u003cbr\u003e ___2.2.2 Loading Data\u003cbr\u003e ___2.2.3 Data Chunking\u003cbr\u003e ___2.2.4 Vector Store\u003cbr\u003e ___2.2.5 Retrievers and Prompts\u003cbr\u003e 2.3 Advanced RAG \u003cbr\u003e___2.3.1 Reranker technology that improves performance by adjusting the order of documents\u003cbr\u003e ___2.3.2 HyDE technology to achieve high query response performance through virtual documents\u003cbr\u003e ___2.3.3 Query expansion techniques to make queries more specific and richer\u003cbr\u003e ___2.3.4 Multi-query technology that can reflect various perspectives\u003cbr\u003e\u003cbr\u003e \u003cb\u003e▣ Chapter 3: Multimodal RAG\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 3.1 _Introducing Multimodal RAG\u003cbr\u003e ___3.1.1 Multimodal RAG Concept\u003cbr\u003e ___3.1.2 Use Cases\u003cbr\u003e ___3.1.3 Importance\u003cbr\u003e 3.2 Key models and technologies of multimodal RAG\u003cbr\u003e ___3.2.1 Multimodal Encoder\u003cbr\u003e ___3.2.2 Decoder for multimodal generation\u003cbr\u003e ___3.2.3 Knowledge Retrieval and Augmentation\u003cbr\u003e ___3.2.4 Convergence Technology\u003cbr\u003e ___3.2.5 Model Training and Fine-Tuning\u003cbr\u003e 3.3 [Practice] Multimodal RAG\u003cbr\u003e ___3.3.1 Multimodal Information Extraction\u003cbr\u003e ___3.3.2 Multimodal RAG Implementation\u003cbr\u003e\u003cbr\u003e \u003cb\u003e▣ Chapter 4: GraphRAG\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 4.1 What is GraphRAG?\u003cbr\u003e 4.2 Differences from the existing RAG\u003cbr\u003e 4.3 Setting up GraphRAG\u003cbr\u003e ___4.3.1 Neo4j\u003cbr\u003e 4.4 [Practice] GraphRAG\u003cbr\u003e ___4.4.1 [Practice] Graph Data Retrieval and Manipulation through Natural Language Queries \u003cbr\u003e___4.4.2 [Practice] LLM-based knowledge graph construction and RAG practice\u003cbr\u003e\u003cbr\u003e \u003cb\u003e▣ Chapter 5: ReAct Agent\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 5.1 What is a ReAct Agent?\u003cbr\u003e ___5.1.1 ReAct Agent Concept\u003cbr\u003e ___5.1.2 Explanation of basic principles\u003cbr\u003e ___5.1.3 Differences between ReAct and the existing method\u003cbr\u003e ___5.1.4 Key Features and Use Cases of ReAct Agent\u003cbr\u003e 5.2 Search API Integration\u003cbr\u003e ___5.2.1 Generating answers based on external data\u003cbr\u003e ___5.2.2 Search API Calls\u003cbr\u003e ___5.2.3 [Practice] Integrating ReAct Agents with the Search API\u003cbr\u003e 5.3 [Practice] Calling Agent-Based Tools\u003cbr\u003e ___5.3.1 Invoking Tools from the Base Agent\u003cbr\u003e ___5.3.2 Calling Advanced Tools Using ReAct\u003cbr\u003e 5.4 Practical Financial Data Projects Using Agents\u003cbr\u003e ___5.4.1 Collecting and Analyzing Financial Data Using ReAct Agents\u003cbr\u003e ___5.4.2 Real-time financial market analysis through search API\u003cbr\u003e ___5.4.3 Integrated Analytics via ReAct Agent\u003cbr\u003e\u003cbr\u003e \u003cb\u003e▣ Chapter 6: sLLM\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 6.1 Reasons to study sLLM\u003cbr\u003e 6.2 Running sLLM\u003cbr\u003e ___6.2.1 Running the sLLM model of Hugging Face\u003cbr\u003e 6.3 FFT learning method and code \u003cbr\u003e___6.3.1 Creating Training Data\u003cbr\u003e ___6.3.2 Parallel processing methods for sLLM learning\u003cbr\u003e ___6.3.3 sLLM Learning Full Fine-tuning\u003cbr\u003e 6.4 PEFT learning method and code\u003cbr\u003e ___6.4.1 PEFT Algorithm and Practice (QLoRA)\u003cbr\u003e ___6.4.2 PEFT Algorithm and Practice (DoRA)\u003cbr\u003e 6.5 RAG-based LLM optimization learning\u003cbr\u003e ___6.5.1 Generating QA data considering RAG\u003cbr\u003e ___6.5.2 Learning sLLM considering RAG\u003cbr\u003e ___6.5.3 sLLM optimization considering RAG\u003cbr\u003e 6.6 LLM Serving\u003cbr\u003e ___6.6.1 Configuring a service environment using Streamlet\u003cbr\u003e ___6.6.2 Deploying sLLM using Streamlet\u003cbr\u003e ___6.6.3 Optimizing sLLM serving with vLLM\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eDetailed image\u003c\/b\u003e \u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cdiv\u003e\u003cimg src=\"https:\/\/image.yes24.com\/momo\/TopCate5227\/MidCate2\/522615655.jpg\" border=\"0\" alt=\"Detailed Image 1\"\u003e\u003c\/div\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e GOODS SPECIFICS \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eDate of issue:\u003c\/strong\u003e April 10, 2025\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003ePage count, weight, size:\u003c\/strong\u003e 300 pages | 175*235*16mm\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN13:\u003c\/strong\u003e 9791158395988 \u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cspan\u003e\u003c\/span\u003e\n\n\u003c\/center\u003e\n\n\n\u003c\/center\u003e","brand":"LIBRAIRIE COREENNE","offers":[{"title":"Default Title","offer_id":43893237252138,"sku":"138410","price":38.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0683\/2750\/5962\/files\/8178f4fc4f76ed5dbeff96517120be78.jpg?v=1765393329","url":"https:\/\/librairie.coreenne.fr\/en\/products\/138410","provider":"LIBRAIRIE COREENNE","version":"1.0","type":"link"}