{"product_id":"138723","title":"A Practical Introduction to RAG and AI Agents Implemented with LangChain and LangGraph ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/3jXPCfB5rsFOpISxUPYXyQCZeDC6BV.png?v=1765067365\" 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 A Practical Introduction to RAG and AI Agents Implemented with LangChain and LangGraph \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\/147123137\/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\u003eMaster everything from RAG design to AI agent design patterns and AI agent practice for each pattern!\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003eThanks to various LLM API services such as OpenAI, Google, and Anthropic, application developers can easily build AI systems.\u003cbr\u003e However, going beyond simple chatbots that interact with people to create truly AI agents that can autonomously perform tasks is a new challenge.\u003cbr\u003e This book guides you step-by-step through OpenAI's Chat API and LangChain basics, all the way to building an advanced AI agent system using LangGraph.\u003cbr\u003e\u003cbr\u003e Beyond simple RAG (Augmented Search Generation) systems, you can build a solid foundation for responding to future advancements in AI technology through AI agent design patterns and implementation code that autonomously perform complex workflows.\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 \",\"\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: Fundamentals of LLM Application Development\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 1.1 Generative AI Begins to Be Used\u003cbr\u003e 1.2 Copilot vs. AI Agent  \u003cbr\u003e1.3 Everything becomes an AI agent\u003cbr\u003e 1.4 Knowledge Map of AI Agents\u003cbr\u003e 1.5 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003e▣ Chapter 2: Basics of the OpenAI Chat API\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 2.1 OpenAI's Chat Model\u003cbr\u003e __ChatGPT's 'Model'\u003cbr\u003e __Chat models available in the OpenAI API\u003cbr\u003e __Model Snapshot\u003cbr\u003e 2.2 OpenAI's Chat API Basics\u003cbr\u003e __Chat Completions API\u003cbr\u003e __Chat Completions API Fees\u003cbr\u003e __Check the incurred charges\u003cbr\u003e 2.3 'Tokens' that affect input\/output length limits and fees\u003cbr\u003e __token\u003cbr\u003e Introducing __Tokenizer and TikToken\u003cbr\u003e __About the number of tokens in Korean\u003cbr\u003e 2.4 Preparing the Chat Completions API Test Environment\u003cbr\u003e __What is Google Colab?\u003cbr\u003e __Create a Google Colab notebook\u003cbr\u003e __Register to use the OpenAI API\u003cbr\u003e __Prepare your OpenAI API key\u003cbr\u003e 2.5 Chat Completions API Practice\u003cbr\u003e __OpenAI library\u003cbr\u003e __Chat Completions API call\u003cbr\u003e __Get responses that take conversation history into account\u003cbr\u003e __Getting responses via streaming\u003cbr\u003e __Basic parameters\u003cbr\u003e __JSON mode\u003cbr\u003e __Vision (image input)\u003cbr\u003e 2.6 Function calling\u003cbr\u003e __Function calling overview\u003cbr\u003e __Function calling sample code\u003cbr\u003e __tool_choice parameter\u003cbr\u003e 2.7 Summary\u003cbr\u003e \u003cbr\u003e\u003cb\u003e▣ Chapter 3: Prompt Engineering\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 3.1 The Need for Prompt Engineering\u003cbr\u003e 3.2 What is Prompt Engineering?\u003cbr\u003e 3.3 Basic components of a prompt\u003cbr\u003e __Topic: Recipe generation AI app\u003cbr\u003e Templating the __prompt\u003cbr\u003e __Separate commands and input data\u003cbr\u003e __Provide context\u003cbr\u003e __Specify output format\u003cbr\u003e __Prompt Component Summary\u003cbr\u003e 3.4 Representative techniques of prompt engineering\u003cbr\u003e __Zero-shot prompting\u003cbr\u003e __Few-shot prompting\u003cbr\u003e __Zero-shot Chain of Thought prompting\u003cbr\u003e 3.5 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003e▣ Chapter 4: LangChain Basics\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 4.1 LangChain Overview\u003cbr\u003e __Why should you learn LangChain?\u003cbr\u003e __LangChain overall structure\u003cbr\u003e __A group of packages that provide various components of LangChain\u003cbr\u003e __Installing LangChain\u003cbr\u003e __LangSmith Settings\u003cbr\u003e __LangChain's main components\u003cbr\u003e 4.2 LLM\/Chat model\u003cbr\u003e __LLM\u003cbr\u003e __Chat model\u003cbr\u003e __Streaming\u003cbr\u003e Inheritance relationship between __LLM and Chat model\u003cbr\u003e __LLM\/Chat model summary\u003cbr\u003e 4.3 Prompt template\u003cbr\u003e __PromptTemplate\u003cbr\u003e __ChatPromptTemplate\u003cbr\u003e __MessagesPlaceholder\u003cbr\u003e __LangSmith's Prompts\u003cbr\u003e __Prompt template summary  \u003cbr\u003e4.4 Output parser\u003cbr\u003e __Output parser overview\u003cbr\u003e Converting Python Objects Using __PydanticOutputParser\u003cbr\u003e __StrOutputParser\u003cbr\u003e __Output parser summary\u003cbr\u003e 4.5 Chain?LangChain Expression Language (LCEL) Overview\u003cbr\u003e __What is LangChain Expression Language (LCEL)?\u003cbr\u003e Connecting __prompt and model\u003cbr\u003e Add __StrOutputParser to the connection\u003cbr\u003e Connecting using __PydanticOutputParser\u003cbr\u003e __Chain Summary\u003cbr\u003e 4.6 LangChain's RAG-related components\u003cbr\u003e __RAG(Retrieval-Augmented Generation)\u003cbr\u003e __Overview of LangChain's RAG-related components\u003cbr\u003e __Document loader\u003cbr\u003e __Document transformer\u003cbr\u003e __Embedding model\u003cbr\u003e __Vector store\u003cbr\u003e __Implementing RAG Chain using LCEL\u003cbr\u003e __Summary of LangChain's RAG-related components\u003cbr\u003e 4.7 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003e▣ Chapter 5: In-Depth Explanation of LangChain Expression Language (LCEL)\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 5.1 Runnable and RunnableSequen\u003cbr\u003e __LCEL's most basic components\u003cbr\u003e __Runnable execution methods―invoke·stream·batch\u003cbr\u003e Connecting various Runnables with '|' in __LCEL\u003cbr\u003e __Checking the inner workings of Chain in LangSmith\u003cbr\u003e 5.2 RunnableLambda - Making an arbitrary function into a runnable  \u003cbr\u003eImplementing RunnableLamda using the __chain decorator\u003cbr\u003e __RunnableLambda automatic conversion\u003cbr\u003e Be careful about the input and output types of __Runnable\u003cbr\u003e 5.3 RunnableParallel - Connecting multiple Runnables in parallel\u003cbr\u003e Connecting the output of __RunnableParallel to the input of Runnable\u003cbr\u003e __RunnableParallel automatic conversion\u003cbr\u003e Combination with __RunnableLambda—Example using itemgetter\u003cbr\u003e 5.4 RunnablePassthrough - Outputting Input as is\u003cbr\u003e __assign―Adding values ​​to the output of RunnableParallel\u003cbr\u003e 5.5 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003e▣ Chapter 6: Advanced RAG\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 6.1 Advanced RAG Overview\u003cbr\u003e 6.2 Preparing for the Lab\u003cbr\u003e 6.3 Search query techniques\u003cbr\u003e Hypothetical Document Embeddings (HyDE)\u003cbr\u003e __Create multiple search queries\u003cbr\u003e __Summary of search query techniques\u003cbr\u003e 6.4 Post-search techniques\u003cbr\u003e __RAG-Fusion\u003cbr\u003e __Rerank Model Overview\u003cbr\u003e __Preparing to use the Cohere Rerank model\u003cbr\u003e __Introducing the Cohere Rerank Model\u003cbr\u003e __Summary of post-search techniques\u003cbr\u003e 6.5 Techniques for Using Multiple Retrievers\u003cbr\u003e Routing by __LLM\u003cbr\u003e __Hybrid search example\u003cbr\u003e __Hybrid search implementation  \u003cbr\u003e__Summary of techniques using multiple retrievers\u003cbr\u003e 6.6 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003e▣ Chapter 7: Evaluating RAG Applications Using LangSmith\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 7.1 Overview of the Evaluation Covered in Chapter 7\u003cbr\u003e __Offline and online evaluations\u003cbr\u003e 7.2 LangSmith Overview\u003cbr\u003e __LangSmith Rate Plan\u003cbr\u003e __LangSmith function overall structure\u003cbr\u003e 7.3 Example of Offline Evaluation Configuration Using LangSmith and Ragas\u003cbr\u003e __Ragas\u003cbr\u003e __Offline evaluation configuration to be built in this chapter\u003cbr\u003e 7.4 Generating synthetic test data using Ragas\u003cbr\u003e __Overview of Ragas' synthetic test data generation capabilities\u003cbr\u003e Installing the __package\u003cbr\u003e __Load the document to be searched\u003cbr\u003e __Implementing synthetic test data generation using Ragas\u003cbr\u003e __Creating LangSmith's Dataset\u003cbr\u003e __Save synthetic test data\u003cbr\u003e 7.5 Implementing Offline Evaluation Using LangSmith and Ragas\u003cbr\u003e __LangSmith's Offline Assessment Overview\u003cbr\u003e __Available Evaluators\u003cbr\u003e __Ragas's evaluation metrics\u003cbr\u003e __Implementing a Custom Evaluator\u003cbr\u003e __Implementing the inference function\u003cbr\u003e __Implementation and execution of offline evaluation\u003cbr\u003e __Offline Evaluation Precautions  \u003cbr\u003e7.6 Collecting Feedback Using LangSmith\u003cbr\u003e __Overview of the feedback features to be implemented in this section\u003cbr\u003e __Implement a function to display the feedback button\u003cbr\u003e __Show feedback button\u003cbr\u003e 7.7 Automatic processing for feedback utilization\u003cbr\u003e Processing using __Automation rules\u003cbr\u003e __Automatically add good evaluation traces to the dataset\u003cbr\u003e 7.8 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003e▣ Chapter 8: What is an AI Agent?\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 8.1 Expectations for LLM Utilization for AI Agents\u003cbr\u003e 8.2 The Origins of AI Agents and Their Evolution Using LLM\u003cbr\u003e __LLM-based AI agent\u003cbr\u003e __WebGPT\u003cbr\u003e __Chain-of-Thought prompting\u003cbr\u003e __MRKL Systems combines LLM and external professional modules\u003cbr\u003e __Reasoning and Acting (ReAct)\u003cbr\u003e __Plan-and-Solve prompting\u003cbr\u003e 8.3 General-Purpose LLM Agent Framework\u003cbr\u003e __AutoGPT\u003cbr\u003e __BabyAGI\u003cbr\u003e __AutoGen\u003cbr\u003e __crewAI\u003cbr\u003e __crewAI's use cases\u003cbr\u003e 8.4 Multi-Agent Approach\u003cbr\u003e __Definition of multi-agent\u003cbr\u003e __Improving Text-to-SQL Accuracy with Multi-Agents\u003cbr\u003e __Automating Software Development with Multi-Agents  \u003cbr\u003e__Self-Organized Agents: Generating and optimizing code at scale\u003cbr\u003e __LLM-based multi-agent framework\u003cbr\u003e 8.5 For AI agents to be distributed safely\u003cbr\u003e 8.6 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003e▣ Chapter 9: Practical Introduction to AI Agents Using LangGraph\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 9.1 LangGraph Overview\u003cbr\u003e __What is LangGraph\u003cbr\u003e __LangGraph graph structure approach\u003cbr\u003e 9.2 Key Components of LangGraph\u003cbr\u003e __state: Represents the state of the graph\u003cbr\u003e __Node: A processing unit that constitutes a graph\u003cbr\u003e __Edge: Connection between nodes\u003cbr\u003e __Compiled graph\u003cbr\u003e 9.3 Hands-on: Q\u0026amp;A Application\u003cbr\u003e __Installing LangChain and LangGraph\u003cbr\u003e __Setting up an OpenAI API key\u003cbr\u003e __Role Definition\u003cbr\u003e __state definition\u003cbr\u003e __Chat model initialization\u003cbr\u003e __node definition\u003cbr\u003e __Graph creation\u003cbr\u003e Add __node\u003cbr\u003e __Edge Definition\u003cbr\u003e __Conditional edge definition\u003cbr\u003e __Graph compilation\u003cbr\u003e __Run graph\u003cbr\u003e __Show results\u003cbr\u003e 9.4 Checkpointing: State Persistence and Resumption\u003cbr\u003e __Checkpoint data structure\u003cbr\u003e __Practice: Verifying Checkpoint Operation\u003cbr\u003e 9.5 Summary\u003cbr\u003e \u003cbr\u003e\u003cb\u003e▣ Chapter 10: Creating a Requirements Definition Document and Developing an AI Agent\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 10.1 Requirements Definition Document Generation AI Agent Overview\u003cbr\u003e __What is requirements definition?\u003cbr\u003e __Refer to the approach of previous research\u003cbr\u003e Designing with LangGraph's Workflow\u003cbr\u003e 10.2 Preferences\u003cbr\u003e 10.3 Defining Data Structures\u003cbr\u003e 10.4 Implementing Key Components\u003cbr\u003e __PersonaGenerator\u003cbr\u003e __InterviewConductor\u003cbr\u003e __InformationEvaluator\u003cbr\u003e __RequirementsDocumentGenerator\u003cbr\u003e 10.5 Building a Workflow\u003cbr\u003e 10.6 Running the Agent and Checking the Results\u003cbr\u003e 10.7 Full source code\u003cbr\u003e 10.8 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003e▣ Chapter 11: Agent Design Patterns\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 11.1 Overview of the Agent Design Pattern\u003cbr\u003e __What is a design pattern?\u003cbr\u003e __Challenge areas addressed by the agent design pattern\u003cbr\u003e __Defining the location of the agent design pattern\u003cbr\u003e __Overall diagram of the agent design pattern\u003cbr\u003e 11.2 18 Agent Design Patterns\u003cbr\u003e __1.\u003cbr\u003e Passive Goal Creator\u003cbr\u003e __2.\u003cbr\u003e Proactive Goal Creator\u003cbr\u003e __3. \u003cbr\u003ePrompt\/Response Optimizer\u003cbr\u003e __4.\u003cbr\u003e Retrieval-Augmented Generation (RAG)\u003cbr\u003e __5.\u003cbr\u003e Single-Path Plan Generator\u003cbr\u003e __6.\u003cbr\u003e Multi-Path Plan Generator\u003cbr\u003e __7.\u003cbr\u003e Self-Reflection\u003cbr\u003e __8.\u003cbr\u003e Cross-Reflection\u003cbr\u003e __9.\u003cbr\u003e Human Reflection\u003cbr\u003e __10.\u003cbr\u003e One-Shot Model Querying\u003cbr\u003e __11.\u003cbr\u003e Incremental Model Querying\u003cbr\u003e __12.\u003cbr\u003e Voting-Based Cooperation\u003cbr\u003e __13.\u003cbr\u003e Role-Based Cooperation\u003cbr\u003e __14.\u003cbr\u003e Debate-Based Cooperation\u003cbr\u003e __15.\u003cbr\u003e Multimodal Guardrails\u003cbr\u003e __16.\u003cbr\u003e Tool\/Agent Registry\u003cbr\u003e __17.\u003cbr\u003e Agent Adapter\u003cbr\u003e __18.\u003cbr\u003e Agent Evaluator\u003cbr\u003e 11.3 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003e▣ Chapter 12: Agent Design Patterns Implemented with LangChain\/LangGraph\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 12.1 Agent Design Patterns Covered in This Chapter  \u003cbr\u003e12.2 Preferences\u003cbr\u003e __About the implementation code for each pattern\u003cbr\u003e 12.3 Passive Goal Creator\u003cbr\u003e __Explanation of implementation details\u003cbr\u003e __Execution result\u003cbr\u003e 12.4 Prompt\/Response Optimizer\u003cbr\u003e __Explanation of implementation details\u003cbr\u003e __Prompt Optimization\u003cbr\u003e __Response Optimization\u003cbr\u003e 12.5 Single Pass Plan Generator\u003cbr\u003e __Explanation of implementation details\u003cbr\u003e __Execution result\u003cbr\u003e 12.6 Multi-Pass Plan Generator\u003cbr\u003e __Explanation of implementation details\u003cbr\u003e __Execution result\u003cbr\u003e 12.7 Self-Reflection\u003cbr\u003e __Explanation of implementation details\u003cbr\u003e __Execution result\u003cbr\u003e 12.8 Cross-Reflection\u003cbr\u003e __Explanation of implementation details\u003cbr\u003e __Execution result\u003cbr\u003e 12.9 Role-Based Cooperation\u003cbr\u003e __Explanation of implementation details\u003cbr\u003e __Execution result\u003cbr\u003e 12.10 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003e▣ Appendix A: Subscription to various services and implementation code for each pattern\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e A.1 Subscription to various services\u003cbr\u003e __LangSmith Join\u003cbr\u003e __Join Cohere\u003cbr\u003e __Anthropic Sign Up\u003cbr\u003e A.2 Implementation code for each pattern\u003cbr\u003e __1.\u003cbr\u003e Passive Goal Creator\u003cbr\u003e __2.\u003cbr\u003e Prompt\/Response Optimizer\u003cbr\u003e __3. \u003cbr\u003eSingle-Path Plan Generator\u003cbr\u003e __4.\u003cbr\u003e Multi-Path Plan Generator\u003cbr\u003e __5.\u003cbr\u003e Self-Reflection\u003cbr\u003e __6.\u003cbr\u003e Cross-Reflection\u003cbr\u003e __7.\u003cbr\u003e Role-Based Cooperation\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\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\/TopCate5347\/MidCate6\/534659959.jpg\" border=\"0\" alt=\"Detailed Image 1\"\u003e\u003c\/div\u003e\u003c\/div\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\u003e★ What this book covers ★\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e ◎ OpenAI's Chat API and Prompt Engineering Fundamentals\u003cbr\u003e ◎ Core components and usage of the Langchain framework\u003cbr\u003e ◎ Advanced implementation techniques and evaluation methods for the RAG system\u003cbr\u003e ◎ Building complex AI agent workflows using lang graphs\u003cbr\u003e ◎ The development process and latest trends of AI agents\u003cbr\u003e ◎ Agent Design Pattern and Practical Implementation Code for Seven Major Patterns\u003cbr\u003e ◎ Development of AI agents capable of complex decision-making processes and autonomous task processing.\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e \"]\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 June 20, 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 508 pages | 188*240*21mm\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 9791158396107 \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":43893276868650,"sku":"138723","price":44.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0683\/2750\/5962\/files\/e683bd01cfcbf5452a46dbc708f328a7.jpg?v=1765395337","url":"https:\/\/librairie.coreenne.fr\/en\/products\/138723","provider":"LIBRAIRIE COREENNE","version":"1.0","type":"link"}