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2941 - 学生开发日:智能 RAG:自主智能与知识检索的结合(自带设备办公).pdf

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1、Orlando,FLOctober 69IBM TechXchange 2025Session code 2941Session code 2941Hailey Thao Quach,Joseph Santarcangelo,Faranak HeidariHailey Thao Quach,Joseph Santarcangelo,Faranak HeidariInformation Developer,Data Scientist,AI DeveloperInformation Developer,Data Scientist,AI DeveloperIBM Skills NetworkIB

2、M Skills NetworkStudent Dev Day:Agentic RAG:Autonomous Intelligence Meets Knowledge Retrieval(sprinkled with a hint of MCP)Agenda0102030405App overviewKey concepts overview+demoHands-On LabTakeawaysQ&AIBM TechXchange|2025 IBM CorporationBefore we start IBM TechXchange|2025 IBM Corporation3The presen

3、tation provides a detailed technical explanation,followed by a 15-minute hands-on lab session to apply what youve learned.0101Feel free to take your time with the lab.If you dont finish during this session,there are instructions at the end of the session to pick up where you left off.0202If you have

4、 questions,dont hesitate to ask during the hands-on lab session!0303Lets begin!What you will build by the end of this sessionIBM TechXchange|2025 IBM Corporation4What you will learn in this session0102030405060708Agentic AI&Multi-agent systemAgentsTasksTools and Tool CallingLangGraphAgentic RAGDocli

5、ngBonus:Model Context Protocol5What is agentic AI?An individual system or An individual system or program program that operates autonomously with agency.It can perform taskstasks,make decisionsmake decisions,and interactinteract with the environment or other systems independently.One AI system One A

6、I system to solve problems or achieve goals on behalf of a user or another system.Often relies on advanced AI models(LLMs,etc.)IBM TechXchange|2025 IBM Corporation6Conventional structure of an AI agentIBM TechXchange|2025 IBM Corporation7What is a multi-agent system(MAS)?A system where multiple auto

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根据报告的内容,全文主要内容概括如下: - **活动信息**:IBM TechXchange 2025,Session代码2941,主题为“Agentic RAG: Autonomous Intelligence Meets Knowledge Retrieval”。 - **议程**:包括App概述、关键概念概述及演示、动手实验室、收获、问答环节。 - **学习内容**: - Agentic AI与多智能体系统(MAS)的概念和结构。 - 智能体、任务、工具和工具调用。 - LangGraph作为LLMs的编排框架。 - Agentic RAG与Docling的使用。 - **工具**:Docling是一个开源工具包,用于解析、转换和理解多种格式的文档。 - **收获**:了解如何构建可扩展和模块化的AI解决方案,以及如何使用Agentic RAG和LangGraph。 - **后续学习**:提供多个相关研讨会和工作坊,以继续学习。
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