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8.0 KiB
JSON
1 line
8.0 KiB
JSON
{"id":"7e61502f-7da6-4650-9161-6a92f2bf6ed7","revision":0,"last_node_id":17,"last_link_id":26,"nodes":[{"id":14,"type":"MultiLinePromptAT","pos":[499.1977233886719,694.01416015625],"size":[423.36444091796875,390.6542053222656],"flags":{},"order":0,"mode":4,"inputs":[{"localized_name":"multi_line_prompt","name":"multi_line_prompt","type":"STRING","widget":{"name":"multi_line_prompt"},"link":null}],"outputs":[{"localized_name":"prompt","name":"prompt","type":"STRING","links":[25]}],"properties":{"Node name for S&R":"MultiLinePromptAT"},"widgets_values":["Introduction to Model Context Protocol (MCP)\nWelcome to Unit 1 of the MCP Course! In this unit, we’ll explore the fundamentals of Model Context Protocol.\n\nWhat You Will Learn\nIn this unit, you will:\n\nUnderstand what Model Context Protocol is and why it’s important\nLearn the key concepts and terminology associated with MCP\nExplore the integration challenges that MCP solves\nWalk through the key benefits and goals of MCP\nSee a simple example of MCP integration in action\nBy the end of this unit, you’ll have a solid understanding of the foundational concepts of MCP and be ready to dive deeper into its architecture and implementation in the next unit.\n\nImportance of MCP\nThe AI ecosystem is evolving rapidly, with Large Language Models (LLMs) and other AI systems becoming increasingly capable. However, these models are often limited by their training data and lack access to real-time information or specialized tools. 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我们将首先定义MCP是什么,并讨论它在AI生态系统中的作用。\n关键概念 - 我们将探讨与MCP相关的基本概念和术语。\n集成挑战 - 我们将研究MCP旨在解决的问题,特别是“M×N集成问题”。\n好处和目标 - 我们将讨论MCP的主要好处和目标,包括标准化,增强的AI功能和互操作性。\n简单示例 - 最后,我们将介绍MCP集成的简单示例,看看它在实践中是如何工作的。\n让我们深入探索模型上下文协议的激动人心的世界!"]},{"id":17,"type":"QuickMTRun","pos":[936.4569702148438,700.031005859375],"size":[270,202],"flags":{},"order":2,"mode":4,"inputs":[{"localized_name":"text","name":"text","type":"STRING","link":25},{"localized_name":"model","name":"model","type":"COMBO","widget":{"name":"model"},"link":null},{"localized_name":"beam_size","name":"beam_size","type":"INT","widget":{"name":"beam_size"},"link":null},{"localized_name":"max_batch_size","name":"max_batch_size","type":"INT","widget":{"name":"max_batch_size"},"link":null},{"localized_name":"temperature","name":"temperature","type":"FLOAT","widget":{"name":"temperature"},"link":null},{"localized_name":"top_k","name":"top_k","type":"INT","widget":{"name":"top_k"},"link":null},{"localized_name":"top_p","name":"top_p","type":"FLOAT","widget":{"name":"top_p"},"link":null},{"localized_name":"unload_model","name":"unload_model","type":"BOOLEAN","widget":{"name":"unload_model"},"link":null}],"outputs":[{"localized_name":"translations","name":"translations","type":"STRING","links":[26]}],"properties":{"Node name for S&R":"QuickMTRun"},"widgets_values":["quickmt-en-zh",1,49,0.4,50,0.9,true]},{"id":13,"type":"QuickMTRun","pos":[921.7299194335938,193.38604736328125],"size":[270,202],"flags":{},"order":3,"mode":0,"inputs":[{"localized_name":"text","name":"text","type":"STRING","link":21},{"localized_name":"model","name":"model","type":"COMBO","widget":{"name":"model"},"link":null},{"localized_name":"beam_size","name":"beam_size","type":"INT","widget":{"name":"beam_size"},"link":null},{"localized_name":"max_batch_size","name":"max_batch_size","type":"INT","widget":{"name":"max_batch_size"},"link":null},{"localized_name":"temperature","name":"temperature","type":"FLOAT","widget":{"name":"temperature"},"link":null},{"localized_name":"top_k","name":"top_k","type":"INT","widget":{"name":"top_k"},"link":null},{"localized_name":"top_p","name":"top_p","type":"FLOAT","widget":{"name":"top_p"},"link":null},{"localized_name":"unload_model","name":"unload_model","type":"BOOLEAN","widget":{"name":"unload_model"},"link":null}],"outputs":[{"localized_name":"translations","name":"translations","type":"STRING","links":[22]}],"properties":{"Node name for S&R":"QuickMTRun"},"widgets_values":["quickmt-zh-en",1,48,0.4,50,0.9,false]}],"links":[[21,2,0,13,0,"STRING"],[22,13,0,3,0,"*"],[25,14,0,17,0,"STRING"],[26,17,0,15,0,"*"]],"groups":[],"config":{},"extra":{"ds":{"scale":0.8264462809917354,"offset":[55.151174649809974,-43.53277915827091]}},"version":0.4} |