{"product_id":"140185","title":"Developing RAG with Langchain: VectorRAG \u0026amp; GraphRAG ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/3jYGWlW5Y8B7XYEZH6qj6XqDY7JIiQ.png?v=1765078610\" 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 Developing RAG with Langchain: VectorRAG \u0026amp; GraphRAG \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\/145605137\/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\u003eA simple example of the differences between VectorRAG and GraphRAG and how to implement them!\u003cbr\u003e We will directly verify the differences in concepts and performance between OpenAI and DeepSeek through hands-on practice.\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e What are the differences between VectorRAG and GraphRAG? And in what data and scenarios are they best suited for use? \"Developing RAG with LangChain: VectorRAG \u0026amp; GraphRAG\" compares the concepts and principles of the two and provides an easily understandable explanation.\u003cbr\u003e We will briefly review the core theory and learn how to implement basic examples using Langchain. \u003cbr\u003eWhen implementing GraphRAG, we will explore several ways to create and retrieve data in Neo4j.\u003cbr\u003e When implementing VectorRAG, we will explore and use not only the OpenAI model but also the DeepSearch model.\u003cbr\u003e You can directly check the performance difference between the two models by downloading the DeepSeek model locally and running it safely.\u003cbr\u003e Through this book, you will learn the basics of RAG and LLM and experience basic implementation methods.\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\u003eChapter 1: Understanding VectorRAG \u0026amp; GraphRAG Concepts\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 1.1 What is RAG?\u003cbr\u003e ____1.1.1 What is RAG?\u003cbr\u003e ____1.1.2 The Need for RAG\u003cbr\u003e ____1.1.3 RAG Core Principles\u003cbr\u003e ____1.1.4 How to implement RAG\u003cbr\u003e 1.2 What is VectorRAG?\u003cbr\u003e ____1.2.1 What is a vector?\u003cbr\u003e ____1.2.2 Vector Processing Process\u003cbr\u003e ____1.2.3 Vector Storage\u003cbr\u003e ____1.2.4 What is VectorRAG?\u003cbr\u003e ____1.2.5 When should I use VectorRAG?\u003cbr\u003e 1.3 What is GraphRAG?  \u003cbr\u003e____1.3.1 What is a graph?\u003cbr\u003e ____1.3.2 GraphDB: Neo4j\u003cbr\u003e ____1.3.3 What is GraphRAG?\u003cbr\u003e ____1.3.4 When do I use GraphRAG?\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 2: Understanding OpenAI Concepts and Principles\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 2.1 What is OpenAI?\u003cbr\u003e 2.2 OpenAI model\u003cbr\u003e ____2.2.1 GPT series\u003cbr\u003e ____2.2.2 ChatGPT\u003cbr\u003e ____2.2.3 DALL·E series\u003cbr\u003e ____2.2.4 Whisper\u003cbr\u003e ____2.2.5 Sora\u003cbr\u003e ____2.2.6 Embedding\u003cbr\u003e 2.3 Principles of ChatGPT\u003cbr\u003e ____2.3.1 What is a transformer?\u003cbr\u003e ____2.3.2 Why Transformers Appeared\u003cbr\u003e 2.4 OpenAI Inference Model: o3-mini\u003cbr\u003e ____2.4.1 Questions that require inference\u003cbr\u003e ____2.4.2 Questions with a clear answer\u003cbr\u003e 2.5 Considerations When Using OpenAI Models\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 3: Understanding DeepSeek Concepts and Principles\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 3.1 What is DeepSeek?\u003cbr\u003e 3.2 Background of DeepSeek's emergence\u003cbr\u003e 3.3 Principles of DeepSeek-R1\u003cbr\u003e 3.4 DeepSeek model\u003cbr\u003e 3.5 Considerations when using DeepSeek models\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 4: Preparing the Practice Environment\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 4.1 Installing and Configuring Anaconda\u003cbr\u003e ____4.1.1 Installing Anaconda\u003cbr\u003e ____4.1.2 Configuring the Anaconda Virtual Environment\u003cbr\u003e 4.2 Preparing an API Key\u003cbr\u003e 4.3 Preparing the DeepSeek Model\u003cbr\u003e 4.4 Installing and Configuring Neo4j  \u003cbr\u003e____4.4.1 Installing Neo4j\u003cbr\u003e ____4.4.2 Learning how to use Neo4j\u003cbr\u003e ____4.4.3 Using Cypher in Neo4j\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 5 VectorRAG Hands-on: Using the OpenAI API\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 5.1 Searching for Car Data\u003cbr\u003e 5.2 Searching Web Data\u003cbr\u003e 5.3 Retrieving Succulent Data from PDF\u003cbr\u003e 5.4 Using Langchain's Memory\u003cbr\u003e 5.5 Retrieving and searching data from multiple files\u003cbr\u003e 5.6 Comparison of Langchain and RamaIndex\u003cbr\u003e 5.7 Cases that are not suitable for VectorRAG\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 6 VectorRAG Hands-on: Using the DeepSeek Model\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 6.1 Searching for car data\u003cbr\u003e 6.2 Searching Web Data\u003cbr\u003e 6.3 Retrieving Succulent Data from PDF\u003cbr\u003e 6.4 Using Langchain's Memory\u003cbr\u003e 6.5 Retrieving and searching data from multiple files\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 7: GraphRAG Practice\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 7.1 Using Graphs in Lang Chains\u003cbr\u003e 7.2 Searching for soccer data\u003cbr\u003e ____7.2.1 Generating Soccer Data\u003cbr\u003e ____7.2.2 Retrieving Soccer Data\u003cbr\u003e 7.3 Importing and Searching PDF Files\u003cbr\u003e 7.4 Searching for movie data\u003cbr\u003e ____7.4.1 Generating Movie Data  \u003cbr\u003e____7.4.2 Searching for movie data\u003cbr\u003e 7.5 Searching for Car Data\u003cbr\u003e ____7.5.1 Creating Car Data\u003cbr\u003e ____7.5.2 Searching for Car Data\u003cbr\u003e 7.6 Searching for health data\u003cbr\u003e ____7.6.1 Creating Health Data\u003cbr\u003e ____7.6.2 Searching for Health Data\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 8: Comparison of Copilot and GraphRAG and the Social Impact of RAG\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 8.1 Comparing Graph-Based Search with GraphRAG in Copilot\u003cbr\u003e ____8.1.1 Searches used in Copilot\u003cbr\u003e ____8.1.2 Comparison of regular RAG and Copilot searches\u003cbr\u003e 8.2 After the RAG Paradigm: What's Next?\u003cbr\u003e ____8.2.1 Limitations and future direction of RAG\u003cbr\u003e ____8.2.2 AI Agents and Reinforcement Learning\u003cbr\u003e 8.3 Social Impact of RAG\u003cbr\u003e ____8.3.1 Trust and Transparency Issues\u003cbr\u003e ____8.3.2 Ethical Considerations of AI\u003cbr\u003e ____8.3.3 Policy and Regulatory Issues\u003cbr\u003e 8.4 Changes in human life\u003cbr\u003e ____8.4.1 Changes in Work\u003cbr\u003e ____8.4.2 Changes in personal life\u003cbr\u003e\u003cbr\u003e Search\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\/TopCate5278\/MidCate1\/527709805.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 \u003cb\u003eSimple, focusing on the core concepts and principles!\u003c\/b\u003e  \u003cbr\u003eIntroduction to RAG by implementing basic examples!\u003cbr\u003e\u003cbr\u003e \u003cb\u003eWhat this book covers: VectorRAG \u0026amp; GraphRAG\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e The core of LLM-related technology is RAG. RAG can be divided into VectorRAG and GraphRAG, depending on the nature of the data.\u003cbr\u003e This book compares and learns the concepts and principles of VectorRAG and GraphRAG, which are similar yet different, including the concept of RAG.\u003cbr\u003e We will examine the scenarios and data in which the VectorRAG method, a common RAG used in the past, and the GraphRAG method, which can identify and utilize relationships between data, can be utilized, and also learn about implementation methods using LangChain.\u003cbr\u003e\u003cbr\u003e \u003cb\u003eWhat this book covers: OpenAI \u0026amp; DeepSeek\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Before we dive into the implementation using Langchain, we'll take a look at the models primarily used in RAG. This book briefly covers the principles and background of the OpenAI model, inference model, and open-source DeepSeek model. \u003cbr\u003eNext, we will use the OpenAI model and the DeepSeek model respectively in the same LangChain code that implemented VectorRAG.\u003cbr\u003e This allows us to actually compare the performance differences between the two models.\u003cbr\u003e DeepSeek models are downloaded and run locally via Ollama for safe use.\u003cbr\u003e\u003cbr\u003e \u003cb\u003eThis book's practical scenario: implementing a basic example.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Let's implement RAG using Langchain in various scenarios.\u003cbr\u003e The examples are easy to follow and are the most basic and easy-to-follow examples, allowing you to understand the differences between OpenAI and DeepSeek models, and between VectorRAG and GraphRAG.\u003cbr\u003e Lastly, since Copilot also utilizes Vector search and Graph search, let's briefly compare the differences between the search methods used in Copilot and VectorRAG and GraphRAG.\u003cbr\u003e\u003cbr\u003e \u003cb\u003e[VectorRAG Example]\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e ㆍ Search for car data\u003cbr\u003e ㆍ Searching web data \u003cbr\u003eㆍ Searching for data in PDF\u003cbr\u003e ㆍUsing Langchain memory\u003cbr\u003e ㆍ Retrieve and search data from multiple files\u003cbr\u003e\u003cbr\u003e \u003cb\u003e[GraphRAG Example]\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e ㆍ Search soccer data\u003cbr\u003e ㆍ Load PDF files and search\u003cbr\u003e ㆍ Search movie data\u003cbr\u003e ㆍ Search for car data\u003cbr\u003e ㆍ Search health data\u003cbr\u003e\u003cbr\u003e \u003cb\u003e[Author's Preface]\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e This book is intended for:\u003cbr\u003e\u003cbr\u003e ㆍ People who want to know the difference between VectorRAG and GraphRAG\u003cbr\u003e ㆍ Developers who want to know how to implement VectorRAG and GraphRAG\u003cbr\u003e ㆍ People who want to know the difference between OpenAI and DeepSeek\u003cbr\u003e ㆍ People who want to check the performance difference between OpenAI and DeepSeek\u003cbr\u003e\u003cbr\u003e No matter how much LLM-based technology develops, RAG can be said to be the core concept that serves as its foundation.\u003cbr\u003e In particular, VectorRAG and GraphRAG play an important role in RAG.\u003cbr\u003e If you understand this concept, you will be able to follow the various technologies that appear later without difficulty. \u003cbr\u003eI hope this book will help lay that foundation. \u003cbr\u003e\n\n\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 April 25, 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 312 pages | 183*235*13mm\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 9791140713240 \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":43893403713578,"sku":"140185","price":39.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0683\/2750\/5962\/files\/54191e1a6aa4f97dd97cf1eb87087cac.jpg?v=1765400533","url":"https:\/\/librairie.coreenne.fr\/en\/products\/140185","provider":"LIBRAIRIE COREENNE","version":"1.0","type":"link"}