{"product_id":"140000","title":"AI applications implemented with LLM and RAG ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/uOtQLAiXjYVEnG0oAyj47y9KrwB.png?v=1765077722\" 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 AI applications implemented with LLM 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\/148861651\/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\u003eAugmented Search Generation (RAG), function calling, agents, vector stores, and the latest framework, MCP!\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e\"Implementing AI Applications with LLM and RAG\" is a comprehensive, practical guide that goes beyond simply following rapidly evolving AI technology trends to provide key insights and practical solutions for actual implementation and integration.\u003cbr\u003e We systematically cover the main trends of generative AI technology from a practical perspective, so that both developers and planners can easily build a RAG system.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e Readers will learn how to flexibly combine and intuitively connect complex components such as document processing, vector indexing, and query routing using RamaIndex.\u003cbr\u003e\n\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 ▣ Chapter 1: Entering the Rama Index\u003cbr\u003e 1.1 Tasks supported by RamaIndex\u003cbr\u003e 1.2 Setting up the RamaIndex usage environment\u003cbr\u003e __1.2.1 Installing Python on Windows\u003cbr\u003e __1.2.2 Creating a virtual environment  \u003cbr\u003e__1.2.3 Installing Visual Studio Code\u003cbr\u003e __1.2.4 Issuing an OpenAI API Key\u003cbr\u003e __1.2.5 Issuing a Gemini API Key\u003cbr\u003e __1.2.6 Adding API Keys to Environment Variables\u003cbr\u003e 1.3 Preview of Rama Index\u003cbr\u003e __1.3.1 Preparing Data\u003cbr\u003e __1.3.2 Installing RamaIndex in a Virtual Environment\u003cbr\u003e __1.3.3 Running RamaIndex\u003cbr\u003e\u003cbr\u003e ▣ Chapter 2: RamaIndex Pipeline\u003cbr\u003e 2.1 Setting up the development environment\u003cbr\u003e 2.2 Data loading\u003cbr\u003e __2.2.1 Data Reader\u003cbr\u003e __2.2.2 Data Connector\u003cbr\u003e 2.3 Text Segmentation\u003cbr\u003e __2.3.1 Documents and Nodes\u003cbr\u003e __2.3.2 Token Unit Splitting\u003cbr\u003e __2.3.3 Sentence unit division\u003cbr\u003e __2.3.4 Semantic Unit Division\u003cbr\u003e __2.3.5 Text Segmentation Comparison\u003cbr\u003e 2.4 Indexing\u003cbr\u003e __2.4.1 What is indexing?\u003cbr\u003e __2.4.2 Vector storage index\u003cbr\u003e __2.4.3 Top-K Search\u003cbr\u003e 2.5 Save\u003cbr\u003e 2.6 Query\u003cbr\u003e __2.6.1 Query Engine (QueryEngine)\u003cbr\u003e __2.6.2 Retrieval\u003cbr\u003e __2.6.3 Postprocessing\u003cbr\u003e __2.6.4 Response synthesis\u003cbr\u003e __2.6.5 Customizing\u003cbr\u003e\u003cbr\u003e ▣ Chapter 3: Vector Store\u003cbr\u003e 3.1 Setting up the development environment\u003cbr\u003e 3.2 Chroma  \u003cbr\u003e__3.2.1 Creating a Chroma Client\u003cbr\u003e __3.2.2 Creating a Collection\u003cbr\u003e __3.2.3 Adding vector data\u003cbr\u003e __3.2.4 Vector Search\u003cbr\u003e __3.2.5 Metadata Filtering\u003cbr\u003e __3.2.6 Adding embedding data\u003cbr\u003e __3.2.7 Searching Embedding Data\u003cbr\u003e __3.2.8 How chroma is stored\u003cbr\u003e __3.2.9 Embedding-based RamaIndex Answer Generation\u003cbr\u003e __3.2.10 Generating answers based on RamaIndex\u003cbr\u003e 3.3 Pinecone\u003cbr\u003e __3.3.1 Pinecon API Initialization\u003cbr\u003e __3.3.2 Adding vector data\u003cbr\u003e __3.3.3 Vector Search\u003cbr\u003e __3.3.4 Metadata Filtering\u003cbr\u003e __3.3.5 Embedding-based RamaIndex Answer Generation\u003cbr\u003e __3.3.6 Generating answers based on RamaIndex (omitting embedding)\u003cbr\u003e 3.4 Quadrant\u003cbr\u003e __3.4.1 Generating answers based on RamaIndex\u003cbr\u003e __3.4.2 Setting up a local environment using Docker\u003cbr\u003e __3.4.3 Cloud-based environment setup\u003cbr\u003e\u003cbr\u003e ▣ Chapter 4: RAG Practice Using Text Documents\u003cbr\u003e 4.1 Setting up the development environment\u003cbr\u003e 4.2 Preparing data for practice\u003cbr\u003e 4.3 Handling PDF Files\u003cbr\u003e __4.3.1 Data Preparation\u003cbr\u003e __4.3.2 Text Segmentation\u003cbr\u003e __4.3.3 Indexing\u003cbr\u003e __4.3.4 Executing a query  \u003cbr\u003e4.4 Handling Text Files\u003cbr\u003e __4.4.1 Basic RAG Practice\u003cbr\u003e __4.4.2 Index Storage: Using Chroma\u003cbr\u003e 4.5 Handling CSV Files\u003cbr\u003e 4.6 Handling HWP files\u003cbr\u003e __4.6.1 Using HWPReader\u003cbr\u003e __4.6.2 Using SimpleDirectoryReader\u003cbr\u003e\u003cbr\u003e ▣ Chapter 5: Multimodal RAG Practice\u003cbr\u003e 5.1 Setting up the development environment\u003cbr\u003e 5.2 Preparing the Data\u003cbr\u003e 5.3 Multimodal Vector Indexing with the OpenAI API\u003cbr\u003e 5.4 Building a Multimodal RAG Using Quadrants\u003cbr\u003e __5.4.1 Quadrant Installation and Client Setup\u003cbr\u003e __5.4.2 Creating a Text and Image Vector Store\u003cbr\u003e __5.4.3 Creating a multimodal vector index\u003cbr\u003e __5.4.4 Search\u003cbr\u003e 5.5 Building a Question-Answering-Based RAG System\u003cbr\u003e __5.5.1 Executing basic queries\u003cbr\u003e __5.5.2 Executing queries using improved prompts\u003cbr\u003e 5.6 Building an Image-Based RAG System\u003cbr\u003e __5.6.1 Downloading and saving new images\u003cbr\u003e __5.6.2 Performing an image search\u003cbr\u003e __5.6.3 Analysis of images with similar painting styles\u003cbr\u003e\u003cbr\u003e ▣ Chapter 6: Agent RAG\u003cbr\u003e 6.1 Setting up the development environment\u003cbr\u003e 6.2 Data Preparation  \u003cbr\u003e6.3 Hugging Face Embedding\u003cbr\u003e 6.4 Creating an Agent\u003cbr\u003e\u003cbr\u003e ▣ Chapter 7: Advanced RAG\u003cbr\u003e 7.1 Setting up the development environment\u003cbr\u003e 7.2 ReRanking\u003cbr\u003e __7.2.1 LLM-based reranking\u003cbr\u003e 7.3 Cost Issues of LLM-Based Reranking\u003cbr\u003e __7.3.1 Reranking based on cross-encoder\u003cbr\u003e 7.4 Hyde\u003cbr\u003e __7.4.1 Data Preparation\u003cbr\u003e __7.4.2 Setting up a large language model and embeddings\u003cbr\u003e __7.4.3 Implementing Hide\u003cbr\u003e\u003cbr\u003e ▣ Chapter 8: Function Calling Agent\u003cbr\u003e 8.1 Setting up the development environment\u003cbr\u003e 8.2 Understanding How Function Calling Works\u003cbr\u003e 8.3 Function calling using external APIs\u003cbr\u003e __8.3.1 Creating a stock market information call agent\u003cbr\u003e __8.3.2 Preparing the function calling tool\u003cbr\u003e __8.3.3 Creating an agent and running a query\u003cbr\u003e 8.4 RAG Agent Implemented with Function Calling\u003cbr\u003e __8.4.1 Environment settings and data preparation\u003cbr\u003e __8.4.2 Preparing the function calling tool\u003cbr\u003e __8.4.3 Creating an agent and executing a query\u003cbr\u003e\u003cbr\u003e ▣ Chapter 9: Implementing a Counselor Agent with Text-to-SQL\u003cbr\u003e 9.1 Setting up the development environment\u003cbr\u003e 9.2 Setting up the environment for agent development  \u003cbr\u003e9.3 Designing a Hospital Database\u003cbr\u003e 9.4 Implementing a Text-to-SQL Agent\u003cbr\u003e 9.5 Multi-turn conversation processing technique\u003cbr\u003e 9.6 User Interface Using Gradio\u003cbr\u003e\u003cbr\u003e ▣ Chapter 10: MCP (Model Context Protocol)\u003cbr\u003e 10.1 What is MCP?\u003cbr\u003e 10.2 Building a Model Context Protocol Development Environment\u003cbr\u003e 10.3 MCP Server\u003cbr\u003e __10.3.1 Registering a tool using an adapter\u003cbr\u003e __10.3.2 MCP Inspector\u003cbr\u003e __10.3.3 Message Format\u003cbr\u003e __10.3.4 Document Search Agent MCP Practice\u003cbr\u003e 10.4 MCP Client\u003cbr\u003e 10.5 Weather Agent Practice\u003cbr\u003e __10.5.1 Obtaining an OpenWeatherMap API Key\u003cbr\u003e __10.5.2 Extracting city names\u003cbr\u003e __10.5.3 OpenWeatherMap API Integration\u003cbr\u003e __10.5.4 Registering MCP Tools and Running the Server\u003cbr\u003e __10.5.5 Implementing an MCP Client: Asking the Weather\u003cbr\u003e __10.5.6 Summary\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\/TopCate5424\/MidCate8\/542370467.jpg\" border=\"0\" alt=\"Detailed Image 1\"\u003e\u003c\/div\u003e\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003ePublisher's Review\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e ★ What this book covers ★\u003cbr\u003e\u003cbr\u003e ◎ Rama Index Pipeline\u003cbr\u003e ◎ Vector Store\u003cbr\u003e ◎ RAG practice using text documents\u003cbr\u003e ◎ Multimodal RAG Practice\u003cbr\u003e ◎ Agent RAG and Advanced RAG \u003cbr\u003e◎ Function Calling Agent\u003cbr\u003e ◎ Consultant agent implemented using Text-to-SQL\u003cbr\u003e ◎ MCP Agent \u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\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 July 18, 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 392 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 9791158396220 \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":43893393162282,"sku":"140000","price":40.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0683\/2750\/5962\/files\/dc0bd790ad0adb5b9b8f1bd1c690b3a4.jpg?v=1765399927","url":"https:\/\/librairie.coreenne.fr\/en\/products\/140000","provider":"LIBRAIRIE COREENNE","version":"1.0","type":"link"}