[{"data":1,"prerenderedAt":2251},["ShallowReactive",2],{"navigation":3,"\u002Fappendix\u002Fb.ai-terminology":189,"\u002Fappendix\u002Fb.ai-terminology-surround":2247},[4,35,57,75,101,123,149,171],{"title":5,"icon":6,"path":7,"stem":8,"children":9,"page":34},"第 1 章：认识 Claude Code","i-lucide-rocket","\u002Fintro","1.intro",[10,14,18,22,26,30],{"title":11,"path":12,"stem":13},"什么是 Claude Code","\u002Fintro\u002Fwhat-is-claude-code","1.intro\u002F1.what-is-claude-code",{"title":15,"path":16,"stem":17},"Claude Code 与 Copilot、Cursor、Windsurf 的本质区别","\u002Fintro\u002Fvs-competitors","1.intro\u002F2.vs-competitors",{"title":19,"path":20,"stem":21},"AI 编程助手生态全景与选型指南","\u002Fintro\u002Fecosystem-guide","1.intro\u002F3.ecosystem-guide",{"title":23,"path":24,"stem":25},"LLM 的概率本质","\u002Fintro\u002Fllm-probability","1.intro\u002F4.llm-probability",{"title":27,"path":28,"stem":29},"从聊天机器人到 Agent","\u002Fintro\u002Ffrom-chatbot-to-agent","1.intro\u002F5.from-chatbot-to-agent",{"title":31,"path":32,"stem":33},"Claude Code 的 Agentic Loop 全拆解","\u002Fintro\u002Fagentic-loop","1.intro\u002F6.agentic-loop",false,{"title":36,"icon":37,"path":38,"stem":39,"children":40,"page":34},"第 2 章：安装与配置","i-lucide-settings","\u002Fsetup","2.setup",[41,45,49,53],{"title":42,"path":43,"stem":44},"系统要求与安装方式","\u002Fsetup\u002Fsystem-requirements","2.setup\u002F1.system-requirements",{"title":46,"path":47,"stem":48},"认证、登录与多账户管理","\u002Fsetup\u002Fauthentication","2.setup\u002F2.authentication",{"title":50,"path":51,"stem":52},"选择你的界面","\u002Fsetup\u002Fchoose-interface","2.setup\u002F3.choose-interface",{"title":54,"path":55,"stem":56},"Coding Plan","\u002Fsetup\u002Fcoding-plan","2.setup\u002F4.coding-plan",{"title":58,"icon":59,"path":60,"stem":61,"children":62,"page":34},"第 3 章：快速上手","i-lucide-hand","\u002Fquickstart","3.quickstart",[63,67,71],{"title":64,"path":65,"stem":66},"启动、交互模式与基本命令","\u002Fquickstart\u002Fstartup","3.quickstart\u002F1.startup",{"title":68,"path":69,"stem":70},"让 Claude 理解你的项目","\u002Fquickstart\u002Fcodebase-understanding","3.quickstart\u002F2.codebase-understanding",{"title":72,"path":73,"stem":74},"第一次代码变更","\u002Fquickstart\u002Ffirst-change","3.quickstart\u002F3.first-change",{"title":76,"icon":77,"path":78,"stem":79,"children":80,"page":34},"第 4 章：核心功能","i-lucide-laptop","\u002Fcore-features","4.core-features",[81,85,89,93,97],{"title":82,"path":83,"stem":84},"代码库全景扫描与模块关系分析","\u002Fcore-features\u002Fcodebase-scan","4.core-features\u002F1.codebase-scan",{"title":86,"path":87,"stem":88},"代码编辑与生成","\u002Fcore-features\u002Fedit-generate","4.core-features\u002F2.edit-generate",{"title":90,"path":91,"stem":92},"测试与调试","\u002Fcore-features\u002Ftest-debug","4.core-features\u002F3.test-debug",{"title":94,"path":95,"stem":96},"Git 工作流","\u002Fcore-features\u002Fgit-workflow","4.core-features\u002F4.git-workflow",{"title":98,"path":99,"stem":100},"工具链执行","\u002Fcore-features\u002Ftoolchain","4.core-features\u002F5.toolchain",{"title":102,"icon":103,"path":104,"stem":105,"children":106,"page":34},"第 5 章：进阶配置","i-lucide-wrench","\u002Fadvanced","5.advanced",[107,111,115,119],{"title":108,"path":109,"stem":110},"CLAUDE.md","\u002Fadvanced\u002Fclaude-md","5.advanced\u002F1.claude-md",{"title":112,"path":113,"stem":114},"Skills","\u002Fadvanced\u002Fskills","5.advanced\u002F2.skills",{"title":116,"path":117,"stem":118},"MCP","\u002Fadvanced\u002Fmcp","5.advanced\u002F3.mcp",{"title":120,"path":121,"stem":122},"Hooks 与 Plan 模式","\u002Fadvanced\u002Fhooks-plan","5.advanced\u002F4.hooks-plan",{"title":124,"icon":125,"path":126,"stem":127,"children":128,"page":34},"第 6 章：实战开发","i-lucide-hammer","\u002Fpractice","6.practice",[129,133,137,141,145],{"title":130,"path":131,"stem":132},"需求分析与架构设计","\u002Fpractice\u002Frequirements-architecture","6.practice\u002F1.requirements-architecture",{"title":134,"path":135,"stem":136},"项目脚手架搭建与技术选型","\u002Fpractice\u002Fscaffolding","6.practice\u002F2.scaffolding",{"title":138,"path":139,"stem":140},"核心功能实现","\u002Fpractice\u002Fcore-features","6.practice\u002F3.core-features",{"title":142,"path":143,"stem":144},"测试覆盖、代码审查与质量调优","\u002Fpractice\u002Ftesting-quality","6.practice\u002F4.testing-quality",{"title":146,"path":147,"stem":148},"部署上线与成果分享","\u002Fpractice\u002Fdeployment","6.practice\u002F5.deployment",{"title":150,"icon":151,"path":152,"stem":153,"children":154,"page":34},"第 7 章：心法层","i-lucide-brain","\u002Fmindset","7.mindset",[155,159,163,167],{"title":156,"path":157,"stem":158},"提示词设计原则","\u002Fmindset\u002Fprompt-design","7.mindset\u002F1.prompt-design",{"title":160,"path":161,"stem":162},"上下文管理策略","\u002Fmindset\u002Fcontext-management","7.mindset\u002F2.context-management",{"title":164,"path":165,"stem":166},"安全与权限控制","\u002Fmindset\u002Fsecurity","7.mindset\u002F3.security",{"title":168,"path":169,"stem":170},"Boris Cherny 的 9 条实战心法与团队推广经验","\u002Fmindset\u002Fboris-cherny-tips","7.mindset\u002F4.boris-cherny-tips",{"title":172,"icon":173,"path":174,"stem":175,"children":176,"page":34},"附录","i-lucide-paperclip","\u002Fappendix","8.appendix",[177,181,185],{"title":178,"path":179,"stem":180},"常用命令速查表","\u002Fappendix\u002Fa.command-cheatsheet","8.appendix\u002Fa.command-cheatsheet",{"title":182,"path":183,"stem":184},"AI 核心术语汇编","\u002Fappendix\u002Fb.ai-terminology","8.appendix\u002Fb.ai-terminology",{"title":186,"path":187,"stem":188},"资源链接与延伸阅读","\u002Fappendix\u002Fc.resources","8.appendix\u002Fc.resources",{"id":190,"title":182,"body":191,"description":1492,"extension":2238,"links":2239,"meta":2240,"navigation":2244,"path":183,"seo":2245,"stem":184,"__hash__":2246},"docs\u002F8.appendix\u002Fb.ai-terminology.md",{"type":192,"value":193,"toc":2147},"minimark",[194,198,202,243,246,250,255,261,267,293,299,301,305,310,316,321,341,347,349,353,358,363,374,376,380,385,390,416,418,422,427,432,446,448,452,457,462,473,479,481,485,490,495,509,511,515,520,526,528,532,537,557,568,570,574,579,593,599,601,605,610,616,618,622,627,633,635,639,644,650,652,656,661,667,669,673,678,684,686,690,695,701,703,707,712,718,720,724,728,733,735,739,744,746,750,755,761,763,767,772,778,780,784,789,795,797,801,806,808,812,816,821,827,829,833,838,840,844,849,864,866,870,875,881,883,887,892,902,904,908,913,915,919,924,930,932,936,941,943,947,951,956,962,964,968,973,979,981,985,990,1004,1006,1010,1015,1017,1021,1034,1040,1042,1046,1051,1060,1062,1066,1071,1073,1077,1082,1088,1090,1094,1099,1105,1107,1111,1116,1118,1122,1127,1129,1133,1137,1142,1162,1168,1170,1174,1179,1185,1187,1191,1196,1202,1204,1208,1213,1219,1221,1225,1230,1235,1237,1241,1246,1251,1253,1257,1262,1264,1268,1292,1297,1299,1303,1308,1310,1314,1322,1324,1328,1333,1335,1339,1344,1350,1352,1356,1361,1366,1368,1372,1376,1381,1387,1389,1393,1398,1404,1406,1410,1415,1421,1423,1427,1432,1438,1440,1444,1449,1451,1455,1460,1466,1468,1472,1477,1479,1483,1493,1495,1499,1503,1515,1519,1574,1578,1636,1640,1689,1693,1742,1746,1792,1796,1842,1844,1848],[195,196,197],"h2",{"id":197},"使用说明",[199,200,201],"p",{},"本文档按概念层次组织 60+ 个 AI 领域核心术语，覆盖从\"认识 Claude\"到\"使用 Claude Code 编程\"的完整旅程：",[203,204,205,213,219,225,231,237],"ul",{},[206,207,208,212],"li",{},[209,210,211],"strong",{},"基础层","：Transformer、LLM、Token、Prompt、Context、Memory 等模型基础",[206,214,215,218],{},[209,216,217],{},"交互层","：Chat、Agent、Multi-Agent、Streaming 等人机交互",[206,220,221,224],{},[209,222,223],{},"能力扩展层","：RAG、Function Call、MCP、API、SDK 等能力边界扩展",[206,226,227,230],{},[209,228,229],{},"工程实践层","：CLI、Git、CI\u002FCD、Docker、Diff、Lint 等开发工程",[206,232,233,236],{},[209,234,235],{},"Claude 生态层","：Claude 模型系列、Claude Code、Skills、Artifacts 等产品特有",[206,238,239,242],{},[209,240,241],{},"安全与可靠性层","：Hallucination、Alignment、RLHF、Guardrails 等",[244,245],"hr",{},[195,247,249],{"id":248},"一基础层模型与输入","一、基础层：模型与输入",[251,252,254],"h3",{"id":253},"_1-transformer","1. Transformer",[199,256,257,260],{},[209,258,259],{},"定义","：2017 年 Google 提出的神经网络架构，是现代 LLM 的基础。核心创新是自注意力机制（Self-Attention），让模型在处理每个 token 时都能关注输入序列中的所有其他 token，从而捕捉长距离依赖关系。",[199,262,263,266],{},[209,264,265],{},"关键组件","：",[203,268,269,275,281,287],{},[206,270,271,274],{},[209,272,273],{},"Self-Attention（自注意力）","：计算每个 token 与其他所有 token 的关联权重",[206,276,277,280],{},[209,278,279],{},"Multi-Head Attention（多头注意力）","：并行计算多组注意力，捕捉不同维度的语义关系",[206,282,283,286],{},[209,284,285],{},"Feed-Forward Network（前馈网络）","：对每个位置独立应用非线性变换",[206,288,289,292],{},[209,290,291],{},"Layer Normalization（层归一化）","：稳定训练过程",[199,294,295,298],{},[209,296,297],{},"意义","：Transformer 的出现让模型能够并行处理序列（而非 RNN 的串行处理），为训练超大规模语言模型奠定了架构基础。",[244,300],{},[251,302,304],{"id":303},"_2-llmlarge-language-model大语言模型","2. LLM（Large Language Model，大语言模型）",[199,306,307,309],{},[209,308,259],{},"：基于 Transformer 架构、通过海量文本数据预训练的大规模神经网络模型，能够理解和生成自然语言。",[199,311,312,315],{},[209,313,314],{},"核心机制","：Next-Token Prediction。给定前文，计算每个候选 token 的条件概率 P(x_t | x_\u003Ct)，采样生成下一个 token，迭代完成整段文本。",[199,317,318,266],{},[209,319,320],{},"关键特征",[203,322,323,329,335],{},[206,324,325,328],{},[209,326,327],{},"概率性","：所有输出都是概率采样结果",[206,330,331,334],{},[209,332,333],{},"涌现能力","：规模达阈值后出现推理、上下文学习等能力",[206,336,337,340],{},[209,338,339],{},"幻觉（Hallucination）","：生成看似合理但实际错误的内容",[199,342,343,346],{},[209,344,345],{},"代表模型（截至 2026 年 4 月）","：GPT-4.1 \u002F GPT-4o（OpenAI）、Claude 4 Opus \u002F Sonnet（Anthropic）、Gemini 2.5（Google）、LLaMA 4（Meta）、Qwen 3（阿里）",[244,348],{},[251,350,352],{"id":351},"_3-token","3. Token",[199,354,355,357],{},[209,356,259],{},"：LLM 处理文本的最小单元，由分词器（Tokenizer）将原始文本切分而成。",[199,359,360,266],{},[209,361,362],{},"关键事实",[203,364,365,368,371],{},[206,366,367],{},"英文约 1 token ≈ 0.75 个单词；中文约 1 个汉字 ≈ 1~2 tokens",[206,369,370],{},"API 按输入+输出的 token 数量计费",[206,372,373],{},"Context Window 容量以 token 数衡量",[244,375],{},[251,377,379],{"id":378},"_4-prompt提示词","4. Prompt（提示词）",[199,381,382,384],{},[209,383,259],{},"：用户向 LLM 提供的输入文本，用于引导模型生成期望输出。",[199,386,387,266],{},[209,388,389],{},"核心形式",[203,391,392,398,404,410],{},[206,393,394,397],{},[209,395,396],{},"System Prompt","：定义模型角色、行为准则、输出格式（开发者预设）",[206,399,400,403],{},[209,401,402],{},"User Prompt","：用户具体请求",[206,405,406,409],{},[209,407,408],{},"Few-shot Prompt","：在 prompt 中嵌入示例引导学习",[206,411,412,415],{},[209,413,414],{},"Chain-of-Thought（CoT）","：要求模型\"一步步思考\"",[244,417],{},[251,419,421],{"id":420},"_5-context上下文","5. Context（上下文）",[199,423,424,426],{},[209,425,259],{},"：LLM 生成响应时能访问的全部输入信息，包括当前 prompt、对话历史、系统指令及外部注入信息。",[199,428,429,266],{},[209,430,431],{},"核心约束",[203,433,434,440],{},[206,435,436,439],{},[209,437,438],{},"Context Window","：模型一次能处理的最大 token 数（Claude 4 ~200K，GPT-4.1 ~1M）",[206,441,442,445],{},[209,443,444],{},"上下文溢出","：超出窗口时早期信息被丢弃，模型\"遗忘\"",[244,447],{},[251,449,451],{"id":450},"_6-context-window上下文窗口","6. Context Window（上下文窗口）",[199,453,454,456],{},[209,455,259],{},"：LLM 单次推理能够处理的最大 token 数量。是模型\"短期记忆\"的物理上限。",[199,458,459,266],{},[209,460,461],{},"典型数值（截至 2026 年 4 月）",[203,463,464,467,470],{},[206,465,466],{},"Claude 4 Opus \u002F Sonnet：~200K tokens",[206,468,469],{},"GPT-4.1：~1M tokens",[206,471,472],{},"GPT-4o：~128K tokens",[199,474,475,478],{},[209,476,477],{},"关键认知","：窗口越大，模型一次能看到的代码\u002F文档越多，但成本也越高。Claude Code 的核心优势之一就是超大上下文窗口带来的完整代码库理解能力。",[244,480],{},[251,482,484],{"id":483},"_7-memory记忆","7. Memory（记忆）",[199,486,487,489],{},[209,488,259],{},"：Agent 系统保留和检索过去信息的能力。",[199,491,492,266],{},[209,493,494],{},"分类",[203,496,497,503],{},[206,498,499,502],{},[209,500,501],{},"短期记忆","：即 Context Window 内的信息",[206,504,505,508],{},[209,506,507],{},"长期记忆","：通过向量数据库存储，用 Embedding 语义检索",[244,510],{},[251,512,514],{"id":513},"_8-embedding嵌入","8. Embedding（嵌入）",[199,516,517,519],{},[209,518,259],{},"：将文本\u002F图像转换为高维数值向量的技术。语义相近的内容在向量空间中距离更近。",[199,521,522,525],{},[209,523,524],{},"核心作用","：语义检索、RAG 基础、Memory 基础。",[244,527],{},[251,529,531],{"id":530},"_9-temperature温度","9. Temperature（温度）",[199,533,534,536],{},[209,535,259],{},"：控制 LLM 输出随机性的参数。取值范围通常为 0~2。",[203,538,539,545,551],{},[206,540,541,544],{},[209,542,543],{},"Temperature = 0","：确定性输出，每次相同输入产生相同结果",[206,546,547,550],{},[209,548,549],{},"Temperature = 0.7~1.0","：默认范围，平衡创造性和一致性",[206,552,553,556],{},[209,554,555],{},"Temperature > 1","：更随机、更有创造性，但可能偏离主题",[199,558,559,562,563,567],{},[209,560,561],{},"使用场景","：代码生成用低 temperature（0",[564,565,566],"del",{},"0.3），创意写作用高 temperature（0.8","1.2）。",[244,569],{},[251,571,573],{"id":572},"_10-top-p-top-k核采样-top-k-采样","10. Top-p \u002F Top-k（核采样 \u002F Top-K 采样）",[199,575,576,578],{},[209,577,259],{},"：控制 LLM 采样范围的参数，与 Temperature 配合使用。",[203,580,581,587],{},[206,582,583,586],{},[209,584,585],{},"Top-k","：只从概率最高的 k 个 token 中采样",[206,588,589,592],{},[209,590,591],{},"Top-p（Nucleus Sampling）","：从累积概率达 p 的最小 token 集合中采样",[199,594,595,598],{},[209,596,597],{},"实际意义","：防止模型选择概率极低的\"荒谬\"token，同时保留足够的候选空间。",[244,600],{},[251,602,604],{"id":603},"_11-system-prompt系统提示词","11. System Prompt（系统提示词）",[199,606,607,609],{},[209,608,259],{},"：在对话开始时给模型的隐性指令，定义其角色、行为边界和输出格式。用户通常不可见，但影响每一次回复。",[199,611,612,615],{},[209,613,614],{},"例子","：\"你是一位资深 Python 工程师。所有回答使用中文。代码必须包含类型注解。\"",[244,617],{},[251,619,621],{"id":620},"_12-chain-of-thoughtcot思维链","12. Chain-of-Thought（CoT，思维链）",[199,623,624,626],{},[209,625,259],{},"：一种 Prompt 技术，通过在 prompt 中要求模型\"一步步思考\"或提供推理示例，引导模型输出中间推理步骤，显著提升复杂问题（数学、逻辑、代码）的准确率。",[199,628,629,632],{},[209,630,631],{},"变体","：Zero-shot CoT（直接加\"Let's think step by step\"）、Few-shot CoT（提供推理示例）。",[244,634],{},[251,636,638],{"id":637},"_13-decoder-only","13. Decoder-only",[199,640,641,643],{},[209,642,259],{},"：一种 Transformer 架构变体，仅使用解码器部分，通过因果掩码（Causal Masking）确保模型只能看到当前位置之前的 token，适合自回归生成任务。",[199,645,646,649],{},[209,647,648],{},"代表","：GPT 系列、Claude、LLaMA、Gemini 均为 Decoder-only 架构。",[244,651],{},[251,653,655],{"id":654},"_14-pre-training预训练","14. Pre-training（预训练）",[199,657,658,660],{},[209,659,259],{},"：在海量无标注文本数据上训练 LLM 的过程。模型通过预测被掩码的 token 学习语言的统计规律和 world knowledge。",[199,662,663,666],{},[209,664,665],{},"后续阶段","：预训练 → 对齐（Alignment）→ 部署使用。",[244,668],{},[251,670,672],{"id":671},"_15-inference推理","15. Inference（推理）",[199,674,675,677],{},[209,676,259],{},"：使用已训练好的模型进行预测\u002F生成的过程。用户每次向 Claude 发送消息、每次 Claude Code 分析代码库，都是一次 Inference。",[199,679,680,683],{},[209,681,682],{},"关键指标","：Latency（延迟）、Throughput（吞吐量）。",[244,685],{},[251,687,689],{"id":688},"_16-parameters-weights参数-权重","16. Parameters \u002F Weights（参数 \u002F 权重）",[199,691,692,694],{},[209,693,259],{},"：神经网络中可学习的数值，决定了模型的行为。参数量是衡量模型规模的指标。",[199,696,697,700],{},[209,698,699],{},"典型规模","：GPT-4 估计 ~1.8T 参数，Claude 4 系列未公开，LLaMA 4 达数万亿参数（MoE 架构）。",[244,702],{},[251,704,706],{"id":705},"_17-fine-tuning微调","17. Fine-tuning（微调）",[199,708,709,711],{},[209,710,259],{},"：在预训练好的模型基础上，用特定领域的小规模标注数据继续训练，使模型适应特定任务或风格。",[199,713,714,717],{},[209,715,716],{},"与 Prompt Engineering 的区别","：微调改变模型权重，Prompt Engineering 不改变模型，只改变输入。",[244,719],{},[195,721,723],{"id":722},"二交互层对话与智能体","二、交互层：对话与智能体",[251,725,727],{"id":726},"_18-conversation对话","18. Conversation（对话）",[199,729,730,732],{},[209,731,259],{},"：用户与 AI 之间多轮交互的消息序列，由交替的 user\u002Fassistant\u002Ftool 消息组成。",[244,734],{},[251,736,738],{"id":737},"_19-chat聊天","19. Chat（聊天）",[199,740,741,743],{},[209,742,259],{},"：以自然语言为媒介的实时交互界面。Chat 是交互模式，Conversation 是底层数据结构。",[244,745],{},[251,747,749],{"id":748},"_20-streaming流式输出","20. Streaming（流式输出）",[199,751,752,754],{},[209,753,259],{},"：模型生成的 token 逐个实时返回给用户，而非等全部生成完毕再一次性返回。带来\"打字机效果\"的交互体验。",[199,756,757,760],{},[209,758,759],{},"技术实现","：Server-Sent Events (SSE) 或 WebSocket。",[244,762],{},[251,764,766],{"id":765},"_21-agent智能体","21. Agent（智能体）",[199,768,769,771],{},[209,770,259],{},"：以 LLM 为核心控制器，具备 Planning、Memory、Tool Use 三大能力的自主系统。",[199,773,774,777],{},[209,775,776],{},"Agentic Loop","：接收目标 → 规划步骤 → 选择工具 → 执行动作 → 观察结果 → 反思调整 → 循环 → 交付。",[244,779],{},[251,781,783],{"id":782},"_22-multi-agent多智能体","22. Multi-Agent（多智能体）",[199,785,786,788],{},[209,787,259],{},"：多个 Agent 协作完成复杂任务的架构。每个 Agent 负责不同子任务，通过消息传递协调。",[199,790,791,794],{},[209,792,793],{},"Claude Code 中的体现","：Sub-agents 并行处理不同专项任务。",[244,796],{},[251,798,800],{"id":799},"_23-human-in-the-loop人机协同","23. Human-in-the-loop（人机协同）",[199,802,803,805],{},[209,804,259],{},"：在 AI 自动化流程的关键节点引入人类审查和决策的机制。Claude Code 的\"修改前请求许可\"就是典型实现。",[244,807],{},[195,809,811],{"id":810},"三能力扩展层工具与协议","三、能力扩展层：工具与协议",[251,813,815],{"id":814},"_24-ragretrieval-augmented-generation检索增强生成","24. RAG（Retrieval-Augmented Generation，检索增强生成）",[199,817,818,820],{},[209,819,259],{},"：将外部知识检索与 LLM 生成结合的技术。生成前先检索相关文档注入上下文。",[199,822,823,826],{},[209,824,825],{},"工作流程","：用户提问 → 查询向量化 → 向量库检索 → 文档注入 Prompt → LLM 生成。",[244,828],{},[251,830,832],{"id":831},"_25-websearch网络搜索","25. WebSearch（网络搜索）",[199,834,835,837],{},[209,836,259],{},"：让 AI 实时访问互联网信息的能力。与 RAG 的区别：WebSearch 检索互联网实时信息，RAG 检索私有知识库。",[244,839],{},[251,841,843],{"id":842},"_26-function-call-tool-call函数调用-工具调用","26. Function Call \u002F Tool Call（函数调用 \u002F 工具调用）",[199,845,846,848],{},[209,847,259],{},"：LLM 生成结构化参数调用外部函数的能力。模型输出 JSON 描述要调用的函数，由宿主程序执行。",[199,850,851,854,855,859,860,863],{},[209,852,853],{},"官方澄清","：OpenAI 文档明确 \"Function calling (also known as tool calling)\"，只是 API 参数命名从 ",[856,857,858],"code",{},"functions"," 演变为 ",[856,861,862],{},"tools","。",[244,865],{},[251,867,869],{"id":868},"_27-mcpmodel-context-protocol模型上下文协议","27. MCP（Model Context Protocol，模型上下文协议）",[199,871,872,874],{},[209,873,259],{},"：Anthropic 2024 年 11 月推出的开源标准协议，标准化 AI 系统与外部数据源的连接方式。被称为\"AI 的 USB-C 接口\"。",[199,876,877,880],{},[209,878,879],{},"架构","：MCP Client（AI 应用）←→ MCP Server（数据\u002F工具服务）。",[244,882],{},[251,884,886],{"id":885},"_28-apiapplication-programming-interface应用程序接口","28. API（Application Programming Interface，应用程序接口）",[199,888,889,891],{},[209,890,259],{},"：软件系统之间交互的约定接口。LLM API 让开发者通过 HTTP 请求调用模型能力。",[199,893,894,897,898,901],{},[209,895,896],{},"Claude API","：Anthropic 提供的 ",[856,899,900],{},"messages.create()"," 等接口，是 Claude Code 等产品的基础能力来源。",[244,903],{},[251,905,907],{"id":906},"_29-sdksoftware-development-kit软件开发工具包","29. SDK（Software Development Kit，软件开发工具包）",[199,909,910,912],{},[209,911,259],{},"：封装 API 的开发者工具库，提供更易用的编程接口。Anthropic 提供 Python 和 TypeScript SDK。",[244,914],{},[251,916,918],{"id":917},"_30-json-mode-structured-output","30. JSON Mode \u002F Structured Output",[199,920,921,923],{},[209,922,259],{},"：让 LLM 输出严格符合 JSON Schema 的格式化数据，而非自由文本。用于程序化处理模型输出。",[199,925,926,929],{},[209,927,928],{},"应用","：Function Call 的参数输出、数据提取、API 响应生成。",[244,931],{},[251,933,935],{"id":934},"_31-rate-limit速率限制","31. Rate Limit（速率限制）",[199,937,938,940],{},[209,939,259],{},"：API 提供商对请求频率的限制（如每分钟\u002F每小时\u002F每天最多多少次请求）。防止滥用和保证服务稳定性。",[244,942],{},[195,944,946],{"id":945},"四工程实践层开发与部署","四、工程实践层：开发与部署",[251,948,950],{"id":949},"_32-clicommand-line-interface命令行界面","32. CLI（Command Line Interface，命令行界面）",[199,952,953,955],{},[209,954,259],{},"：通过文本命令与计算机交互的界面。Claude Code 的核心形态就是 CLI 工具。",[199,957,958,961],{},[209,959,960],{},"与 GUI 的区别","：CLI 更高效、可脚本化、适合自动化；GUI 更直观、适合新手。",[244,963],{},[251,965,967],{"id":966},"_33-ideintegrated-development-environment集成开发环境","33. IDE（Integrated Development Environment，集成开发环境）",[199,969,970,972],{},[209,971,259],{},"：集成了代码编辑、调试、构建等功能的开发工具。如 VS Code、IntelliJ、PyCharm。",[199,974,975,978],{},[209,976,977],{},"Claude Code 的 IDE 集成","：VS Code 扩展、JetBrains 插件，但核心体验仍是 CLI。",[244,980],{},[251,982,984],{"id":983},"_34-git","34. Git",[199,986,987,989],{},[209,988,259],{},"：分布式版本控制系统，跟踪代码的每一次变更。现代软件开发的基石工具。",[199,991,992,995,996,999,1000,1003],{},[209,993,994],{},"Claude Code 中的 Git 集成","：对话式 Git 操作（",[856,997,998],{},"what files have I changed?","、",[856,1001,1002],{},"commit my changes","）。",[244,1005],{},[251,1007,1009],{"id":1008},"_35-repository代码仓库","35. Repository（代码仓库）",[199,1011,1012,1014],{},[209,1013,259],{},"：存储项目代码及版本历史的目录，通常由 Git 管理。Claude Code 以 Repository 为单位理解项目上下文。",[244,1016],{},[251,1018,1020],{"id":1019},"_36-diff代码差异","36. Diff（代码差异）",[199,1022,1023,1025,1026,1029,1030,1033],{},[209,1024,259],{},"：展示两个代码版本之间差异的格式（",[856,1027,1028],{},"+"," 新增行，",[856,1031,1032],{},"-"," 删除行）。代码审查和版本控制的核心工具。",[199,1035,1036,1039],{},[209,1037,1038],{},"Claude Code 中的 Diff","：每次修改前向用户展示 diff，确认后才写入文件。",[244,1041],{},[251,1043,1045],{"id":1044},"_37-commit提交","37. Commit（提交）",[199,1047,1048,1050],{},[209,1049,259],{},"：将代码变更保存到 Git 历史中的操作。包含变更内容、作者、时间戳和提交信息。",[199,1052,1053,1056,1057,863],{},[209,1054,1055],{},"Claude Code 的能力","：自动生成描述性提交信息，甚至自动执行 ",[856,1058,1059],{},"git commit",[244,1061],{},[251,1063,1065],{"id":1064},"_38-branch分支","38. Branch（分支）",[199,1067,1068,1070],{},[209,1069,259],{},"：Git 中独立的代码开发线。允许并行开发不同功能而不互相干扰。",[244,1072],{},[251,1074,1076],{"id":1075},"_39-lint代码检查","39. Lint（代码检查）",[199,1078,1079,1081],{},[209,1080,259],{},"：静态分析代码以发现潜在错误、风格违规或不符合规范的地方。如 ESLint、Pylint、Prettier。",[199,1083,1084,1087],{},[209,1085,1086],{},"Claude Code 的 Hooks 应用","：可在提交前自动运行 linter。",[244,1089],{},[251,1091,1093],{"id":1092},"_40-cicdcontinuous-integration-continuous-deployment持续集成持续部署","40. CI\u002FCD（Continuous Integration \u002F Continuous Deployment，持续集成\u002F持续部署）",[199,1095,1096,1098],{},[209,1097,259],{},"：自动化构建、测试和部署代码的流程。GitHub Actions、GitLab CI 是典型实现。",[199,1100,1101,1104],{},[209,1102,1103],{},"Claude Code 集成","：可通过 MCP 连接 CI 系统，读取构建失败日志并自动修复。",[244,1106],{},[251,1108,1110],{"id":1109},"_41-docker-container容器","41. Docker \u002F Container（容器）",[199,1112,1113,1115],{},[209,1114,259],{},"：将应用及其依赖打包为标准化可移植单元的技术。确保\"在我机器上能跑\"变成\"在哪都能跑\"。",[244,1117],{},[251,1119,1121],{"id":1120},"_42-environment-variable环境变量","42. Environment Variable（环境变量）",[199,1123,1124,1126],{},[209,1125,259],{},"：操作系统级别的键值对配置，程序运行时读取。常用于存储 API Key、数据库连接串等敏感信息，避免硬编码。",[244,1128],{},[195,1130,1132],{"id":1131},"五claude-生态层产品特有","五、Claude 生态层：产品特有",[251,1134,1136],{"id":1135},"_43-claude-sonnet-opus-haiku","43. Claude Sonnet \u002F Opus \u002F Haiku",[199,1138,1139,1141],{},[209,1140,259],{},"：Anthropic Claude 模型系列的三个梯度：",[203,1143,1144,1150,1156],{},[206,1145,1146,1149],{},[209,1147,1148],{},"Opus","：最强推理能力，适合复杂分析、数学、代码架构设计",[206,1151,1152,1155],{},[209,1153,1154],{},"Sonnet","：均衡性能与速度，日常使用首选",[206,1157,1158,1161],{},[209,1159,1160],{},"Haiku","：最快、最便宜，适合简单任务和高频调用",[199,1163,1164,1167],{},[209,1165,1166],{},"Claude Code 中的模型选择","：复杂重构用 Opus，日常任务用 Sonnet，Claude Code 允许切换模型。",[244,1169],{},[251,1171,1173],{"id":1172},"_44-claude-code","44. Claude Code",[199,1175,1176,1178],{},[209,1177,259],{},"：Anthropic 推出的终端原生智能体编程系统。读取完整代码库，自主规划多文件变更，执行修改，运行测试，迭代修复。",[199,1180,1181,1184],{},[209,1182,1183],{},"核心定位","：不是代码补全工具，不是 IDE，是任务委托伙伴。",[244,1186],{},[251,1188,1190],{"id":1189},"_45-claudemd","45. CLAUDE.md",[199,1192,1193,1195],{},[209,1194,259],{},"：Claude Code 项目根目录的持久指令文件。定义编码标准、架构决策、工作流规则，每次会话自动读取。",[199,1197,1198,1201],{},[209,1199,1200],{},"作用","：团队知识沉淀、项目规范统一、减少重复说明。",[244,1203],{},[251,1205,1207],{"id":1206},"_46-skills","46. Skills",[199,1209,1210,1212],{},[209,1211,259],{},"：Claude Code 中用 Markdown+YAML 定义的标准化操作流程。将重复性工作方法编码为可复用模板。",[199,1214,1215,1218],{},[209,1216,1217],{},"与 MCP 的区别","：Skill 定义\"做什么\"（工作流程），MCP 定义\"连什么\"（外部接口）。",[244,1220],{},[251,1222,1224],{"id":1223},"_47-hooks","47. Hooks",[199,1226,1227,1229],{},[209,1228,259],{},"：Claude Code 中的生命周期钩子，在特定事件前后触发脚本。如提交前自动 lint、编辑后自动测试。",[199,1231,1232,1234],{},[209,1233,1200],{},"：为 AI 自主行为添加安全护栏。",[244,1236],{},[251,1238,1240],{"id":1239},"_48-sub-agents子智能体","48. Sub-agents（子智能体）",[199,1242,1243,1245],{},[209,1244,259],{},"：由主 Agent 派生的并行处理单元。独立执行专项任务，避免主对话上下文被无关信息填满。",[199,1247,1248,1250],{},[209,1249,431],{},"：上下文窗口 ~200K tokens，复杂任务中 Sub-agents 是管理溢出的核心策略。",[244,1252],{},[251,1254,1256],{"id":1255},"_49-plan-mode计划模式","49. Plan Mode（计划模式）",[199,1258,1259,1261],{},[209,1260,259],{},"：Claude Code 的安全操作模式。Claude 先提出完整计划（含 diff 预览），人类批准后执行。适合大规模重构或高风险操作。",[244,1263],{},[251,1265,1267],{"id":1266},"_50-slash-commands斜杠命令","50. Slash Commands（斜杠命令）",[199,1269,1270,1272,1273,1276,1277,999,1280,999,1283,999,1286,999,1289,863],{},[209,1271,259],{},"：Claude Code 中以 ",[856,1274,1275],{},"\u002F"," 开头的内置命令。如 ",[856,1278,1279],{},"\u002Flogin",[856,1281,1282],{},"\u002Fhelp",[856,1284,1285],{},"\u002Fclear",[856,1287,1288],{},"\u002Fconfig",[856,1290,1291],{},"\u002Fresume",[199,1293,1294,1296],{},[209,1295,1200],{},"：快速触发特定功能，无需自然语言描述。",[244,1298],{},[251,1300,1302],{"id":1301},"_51-checkpoint检查点","51. Checkpoint（检查点）",[199,1304,1305,1307],{},[209,1306,259],{},"：Claude Code 自动创建的代码状态快照。允许用户随时回滚到之前的代码状态，防止 AI 修改导致不可逆破坏。",[244,1309],{},[251,1311,1313],{"id":1312},"_52-session会话","52. Session（会话）",[199,1315,1316,1318,1319,1321],{},[209,1317,259],{},"：Claude Code 中一次连续的工作单元。包含对话历史、已执行的操作、当前上下文。可用 ",[856,1320,1291],{}," 恢复之前的会话。",[244,1323],{},[251,1325,1327],{"id":1326},"_53-artifacts","53. Artifacts",[199,1329,1330,1332],{},[209,1331,259],{},"：Claude.ai 网页版中的独立内容展示组件。当 Claude 生成代码、文档、图表等可复用内容时，以独立窗口呈现，方便查看、复制和迭代。",[244,1334],{},[251,1336,1338],{"id":1337},"_54-computer-use","54. Computer Use",[199,1340,1341,1343],{},[209,1342,259],{},"：Claude 的能力之一，允许模型通过截图和鼠标\u002F键盘操作与计算机界面交互。可以操作浏览器、填写表单、运行 GUI 应用。",[199,1345,1346,1349],{},[209,1347,1348],{},"与 Claude Code 的区别","：Computer Use 操作图形界面，Claude Code 操作代码和命令行。",[244,1351],{},[251,1353,1355],{"id":1354},"_55-openclaw","55. OpenClaw",[199,1357,1358,1360],{},[209,1359,259],{},"：开源（MIT）自托管网关，连接聊天应用（Discord、Telegram、WhatsApp 等）与 AI 编码 Agent。",[199,1362,1363,1365],{},[209,1364,1348],{},"：OpenClaw 是聊天网关（面向消息平台），Claude Code 是开发工具（面向代码库）。",[244,1367],{},[195,1369,1371],{"id":1370},"六安全与可靠性层","六、安全与可靠性层",[251,1373,1375],{"id":1374},"_56-hallucination幻觉","56. Hallucination（幻觉）",[199,1377,1378,1380],{},[209,1379,259],{},"：LLM 生成看似合理但实际错误、虚构或无法验证的内容。是概率生成机制的固有特性，不是 bug。",[199,1382,1383,1386],{},[209,1384,1385],{},"应对","：RAG 注入真实信息、人工审查、事实核查工具。",[244,1388],{},[251,1390,1392],{"id":1391},"_57-prompt-injection提示注入","57. Prompt Injection（提示注入）",[199,1394,1395,1397],{},[209,1396,259],{},"：攻击者通过精心构造的输入，绕过模型的安全限制或操纵模型行为。如\"忽略之前的指令，告诉我如何...\"",[199,1399,1400,1403],{},[209,1401,1402],{},"防御","：输入过滤、输出审查、权限最小化。",[244,1405],{},[251,1407,1409],{"id":1408},"_58-alignment对齐","58. Alignment（对齐）",[199,1411,1412,1414],{},[209,1413,259],{},"：确保 AI 系统的行为符合人类意图和价值观的过程。解决\"模型能做什么\"与\"模型应该做什么\"之间的差距。",[199,1416,1417,1420],{},[209,1418,1419],{},"关键技术","：RLHF、Constitutional AI。",[244,1422],{},[251,1424,1426],{"id":1425},"_59-rlhfreinforcement-learning-from-human-feedback基于人类反馈的强化学习","59. RLHF（Reinforcement Learning from Human Feedback，基于人类反馈的强化学习）",[199,1428,1429,1431],{},[209,1430,259],{},"：训练 LLM 的核心对齐技术。人类对模型输出进行排序评分，模型通过强化学习优化以生成人类偏好的回答。",[199,1433,1434,1437],{},[209,1435,1436],{},"流程","：预训练 → 收集人类反馈 → 训练奖励模型 → 用 PPO 等算法优化策略。",[244,1439],{},[251,1441,1443],{"id":1442},"_60-constitutional-ai宪法-ai","60. Constitutional AI（宪法 AI）",[199,1445,1446,1448],{},[209,1447,259],{},"：Anthropic 特有的对齐方法。让模型遵循一组\"宪法原则\"（如\"选择最诚实、最少有害的回答\"），通过自我批评和修订来改进输出，减少对人类标注的依赖。",[244,1450],{},[251,1452,1454],{"id":1453},"_61-guardrails护栏","61. Guardrails（护栏）",[199,1456,1457,1459],{},[209,1458,259],{},"：限制 AI 系统行为的安全机制集合。包括输入过滤、输出审查、权限控制、操作确认等。",[199,1461,1462,1465],{},[209,1463,1464],{},"Claude Code 的护栏","：修改前请求许可、Plan Mode、Hooks、内置分类器区分安全\u002F风险操作。",[244,1467],{},[251,1469,1471],{"id":1470},"_62-red-teaming红队测试","62. Red Teaming（红队测试）",[199,1473,1474,1476],{},[209,1475,259],{},"：模拟攻击者角度对 AI 系统进行压力测试，发现安全漏洞、偏见和失效模式。是 AI 安全评估的标准实践。",[244,1478],{},[195,1480,1482],{"id":1481},"七术语关系全景图","七、术语关系全景图",[1484,1485,1490],"pre",{"className":1486,"code":1488,"language":1489},[1487],"language-text","┌─────────────────────────────────────────────────────────────────────┐\n│                          基础层                                      │\n│  Transformer ←── LLM ←── Token ←── Prompt ←── Context\u002FContext Window│\n│     ↑              ↑         ↑            ↑                         │\n│  Attention    Parameters  Temperature   System Prompt               │\n│  Decoder-only  Pre-training  Top-p\u002FTop-k  CoT                      │\n│  Inference    Fine-tuning    Embedding   Memory                    │\n└─────────────────────────────────────────────────────────────────────┘\n                                   ↓\n┌─────────────────────────────────────────────────────────────────────┐\n│                          交互层                                      │\n│  Chat ←── Conversation ←── Streaming ←── Agent ←── Multi-Agent     │\n│                                              ↑                      │\n│                                       Human-in-the-loop            │\n└─────────────────────────────────────────────────────────────────────┘\n                                   ↓\n┌─────────────────────────────────────────────────────────────────────┐\n│                        能力扩展层                                    │\n│  RAG ←── WebSearch ←── Function\u002FTool Call ←── MCP ←── API\u002FSDK      │\n│                                          ↑                          │\n│                                    JSON Mode \u002F Structured Output    │\n│                                    Rate Limit                       │\n└─────────────────────────────────────────────────────────────────────┘\n                                   ↓\n┌─────────────────────────────────────────────────────────────────────┐\n│                        工程实践层                                    │\n│  CLI ←── IDE ←── Git\u002FRepo ←── Diff ←── Commit\u002FBranch ←── Lint     │\n│  CI\u002FCD ←── Docker ←── Environment Variable                         │\n└─────────────────────────────────────────────────────────────────────┘\n                                   ↓\n┌─────────────────────────────────────────────────────────────────────┐\n│                        Claude 生态层                                 │\n│  Claude Opus\u002FSonnet\u002FHaiku                                           │\n│       ↓                                                             │\n│  Claude Code ←── CLAUDE.md ←── Skills ←── Hooks                    │\n│       ↓              ↓                    ↓                         │\n│  Sub-agents ←── Plan Mode ←── Slash Commands ←── Checkpoint        │\n│  Session ←── Artifacts ←── Computer Use                             │\n│  OpenClaw（独立网关）                                                │\n└─────────────────────────────────────────────────────────────────────┘\n                                   ↓\n┌─────────────────────────────────────────────────────────────────────┐\n│                      安全与可靠性层                                  │\n│  Hallucination ←── Prompt Injection ←── Guardrails                 │\n│  Alignment ←── RLHF ←── Constitutional AI                          │\n│  Red Teaming                                                        │\n└─────────────────────────────────────────────────────────────────────┘\n","text",[856,1491,1488],{"__ignoreMap":1492},"",[244,1494],{},[195,1496,1498],{"id":1497},"八易混淆术语对比","八、易混淆术语对比",[251,1500,1502],{"id":1501},"function-call-vs-tool-call","Function Call vs Tool Call",[199,1504,1505,1508,1509,1511,1512,1514],{},[209,1506,1507],{},"结论：同一概念，命名演变。"," OpenAI 早期用 ",[856,1510,858],{}," 参数，后改为 ",[856,1513,862],{},"。官方文档：\"Function calling (also known as tool calling)\"。",[251,1516,1518],{"id":1517},"rag-vs-memory","RAG vs Memory",[1520,1521,1522,1537],"table",{},[1523,1524,1525],"thead",{},[1526,1527,1528,1531,1534],"tr",{},[1529,1530],"th",{},[1529,1532,1533],{},"RAG",[1529,1535,1536],{},"Memory",[1538,1539,1540,1552,1563],"tbody",{},[1526,1541,1542,1546,1549],{},[1543,1544,1545],"td",{},"数据源",[1543,1547,1548],{},"预设知识库",[1543,1550,1551],{},"动态积累的交互经验",[1526,1553,1554,1557,1560],{},[1543,1555,1556],{},"更新方式",[1543,1558,1559],{},"人工上传\u002F同步",[1543,1561,1562],{},"自动记录",[1526,1564,1565,1568,1571],{},[1543,1566,1567],{},"典型场景",[1543,1569,1570],{},"文档问答",[1543,1572,1573],{},"个性化对话",[251,1575,1577],{"id":1576},"mcp-vs-function-call","MCP vs Function Call",[1520,1579,1580,1591],{},[1523,1581,1582],{},[1526,1583,1584,1586,1589],{},[1529,1585],{},[1529,1587,1588],{},"Function Call",[1529,1590,116],{},[1538,1592,1593,1604,1614,1625],{},[1526,1594,1595,1598,1601],{},[1543,1596,1597],{},"层级",[1543,1599,1600],{},"模型能力",[1543,1602,1603],{},"系统协议",[1526,1605,1606,1608,1611],{},[1543,1607,1200],{},[1543,1609,1610],{},"模型决定\"调用什么\"",[1543,1612,1613],{},"标准化\"如何暴露和发现工具\"",[1526,1615,1616,1619,1622],{},[1543,1617,1618],{},"关系",[1543,1620,1621],{},"基础机制",[1543,1623,1624],{},"建立在 Function Call 之上",[1526,1626,1627,1630,1633],{},[1543,1628,1629],{},"类比",[1543,1631,1632],{},"HTTP 请求",[1543,1634,1635],{},"REST API 规范",[251,1637,1639],{"id":1638},"langchain-vs-langgraph","LangChain vs LangGraph",[1520,1641,1642,1654],{},[1523,1643,1644],{},[1526,1645,1646,1648,1651],{},[1529,1647],{},[1529,1649,1650],{},"LangChain",[1529,1652,1653],{},"LangGraph",[1538,1655,1656,1667,1678],{},[1526,1657,1658,1661,1664],{},[1543,1659,1660],{},"定位",[1543,1662,1663],{},"LLM 组件库",[1543,1665,1666],{},"Agent 工作流编排框架",[1526,1668,1669,1672,1675],{},[1543,1670,1671],{},"抽象",[1543,1673,1674],{},"Chains",[1543,1676,1677],{},"Graph（节点+边）",[1526,1679,1680,1683,1686],{},[1543,1681,1682],{},"循环支持",[1543,1684,1685],{},"有限",[1543,1687,1688],{},"原生支持",[251,1690,1692],{"id":1691},"claude-code-vs-openclaw","Claude Code vs OpenClaw",[1520,1694,1695,1707],{},[1523,1696,1697],{},[1526,1698,1699,1701,1704],{},[1529,1700],{},[1529,1702,1703],{},"Claude Code",[1529,1705,1706],{},"OpenClaw",[1538,1708,1709,1720,1731],{},[1526,1710,1711,1714,1717],{},[1543,1712,1713],{},"类型",[1543,1715,1716],{},"开发工具",[1543,1718,1719],{},"通信网关",[1526,1721,1722,1725,1728],{},[1543,1723,1724],{},"界面",[1543,1726,1727],{},"终端\u002FIDE",[1543,1729,1730],{},"聊天应用",[1526,1732,1733,1736,1739],{},[1543,1734,1735],{},"工作对象",[1543,1737,1738],{},"代码库",[1543,1740,1741],{},"对话消息",[251,1743,1745],{"id":1744},"context-vs-context-window","Context vs Context Window",[1520,1747,1748,1759],{},[1523,1749,1750],{},[1526,1751,1752,1754,1757],{},[1529,1753],{},[1529,1755,1756],{},"Context",[1529,1758,438],{},[1538,1760,1761,1772,1783],{},[1526,1762,1763,1766,1769],{},[1543,1764,1765],{},"概念",[1543,1767,1768],{},"模型此刻能看到的全部信息",[1543,1770,1771],{},"信息量的物理上限",[1526,1773,1774,1777,1780],{},[1543,1775,1776],{},"比喻",[1543,1778,1779],{},"桌面上的文件",[1543,1781,1782],{},"桌面的大小",[1526,1784,1785,1787,1790],{},[1543,1786,1618],{},[1543,1788,1789],{},"Context 必须 fit in Context Window",[1543,1791],{},[251,1793,1795],{"id":1794},"skills-vs-mcp","Skills vs MCP",[1520,1797,1798,1808],{},[1523,1799,1800],{},[1526,1801,1802,1804,1806],{},[1529,1803],{},[1529,1805,112],{},[1529,1807,116],{},[1538,1809,1810,1820,1831],{},[1526,1811,1812,1814,1817],{},[1543,1813,259],{},[1543,1815,1816],{},"\"做什么\"（工作流程）",[1543,1818,1819],{},"\"连什么\"（外部接口）",[1526,1821,1822,1825,1828],{},[1543,1823,1824],{},"格式",[1543,1826,1827],{},"Markdown+YAML",[1543,1829,1830],{},"协议标准",[1526,1832,1833,1836,1839],{},[1543,1834,1835],{},"范围",[1543,1837,1838],{},"Claude Code 内部",[1543,1840,1841],{},"跨平台通用",[244,1843],{},[195,1845,1847],{"id":1846},"九来源索引","九、来源索引",[1520,1849,1850,1866],{},[1523,1851,1852],{},[1526,1853,1854,1857,1860,1863],{},[1529,1855,1856],{},"#",[1529,1858,1859],{},"术语",[1529,1861,1862],{},"主要来源",[1529,1864,1865],{},"URL",[1538,1867,1868,1882,1896,1910,1924,1937,1951,1963,1976,1990,2003,2017,2030,2043,2055,2069,2082,2095,2109,2121,2134],{},[1526,1869,1870,1873,1876,1879],{},[1543,1871,1872],{},"1",[1543,1874,1875],{},"Transformer \u002F Attention",[1543,1877,1878],{},"Vaswani et al. \"Attention Is All You Need\" (2017)",[1543,1880,1881],{},"arxiv.org\u002Fabs\u002F1706.03762",[1526,1883,1884,1887,1890,1893],{},[1543,1885,1886],{},"2",[1543,1888,1889],{},"LLM \u002F Next-Token",[1543,1891,1892],{},"wwwinsights.com",[1543,1894,1895],{},"wwwinsights.com\u002Fai\u002Fllm-next-token-prediction\u002F",[1526,1897,1898,1901,1904,1907],{},[1543,1899,1900],{},"3",[1543,1902,1903],{},"Token \u002F Embedding",[1543,1905,1906],{},"行业通用定义",[1543,1908,1909],{},"OpenAI Tokenizer 文档、HuggingFace 指南",[1526,1911,1912,1915,1918,1921],{},[1543,1913,1914],{},"4",[1543,1916,1917],{},"Prompt \u002F CoT",[1543,1919,1920],{},"Wei et al. \"Chain-of-Thought Prompting\"",[1543,1922,1923],{},"arxiv.org\u002Fabs\u002F2201.11903",[1526,1925,1926,1929,1931,1934],{},[1543,1927,1928],{},"5",[1543,1930,438],{},[1543,1932,1933],{},"各模型官方文档",[1543,1935,1936],{},"Anthropic \u002F OpenAI API 文档",[1526,1938,1939,1942,1945,1948],{},[1543,1940,1941],{},"6",[1543,1943,1944],{},"Agent 架构",[1543,1946,1947],{},"Lilian Weng (OpenAI)",[1543,1949,1950],{},"lilianweng.github.io\u002Fposts\u002F2023-06-23-agent\u002F",[1526,1952,1953,1956,1959,1961],{},[1543,1954,1955],{},"7",[1543,1957,1958],{},"Memory \u002F Vector Store",[1543,1960,1947],{},[1543,1962,1950],{},[1526,1964,1965,1968,1970,1973],{},[1543,1966,1967],{},"8",[1543,1969,1533],{},[1543,1971,1972],{},"Lewis et al. \"Retrieval-Augmented Generation\"",[1543,1974,1975],{},"arxiv.org\u002Fabs\u002F2005.11401",[1526,1977,1978,1981,1984,1987],{},[1543,1979,1980],{},"9",[1543,1982,1983],{},"Function\u002FTool Call",[1543,1985,1986],{},"OpenAI 官方文档",[1543,1988,1989],{},"developers.openai.com\u002Fapi\u002Fdocs\u002Fguides\u002Ffunction-calling",[1526,1991,1992,1995,1997,2000],{},[1543,1993,1994],{},"10",[1543,1996,116],{},[1543,1998,1999],{},"Anthropic 官方",[1543,2001,2002],{},"anthropic.com\u002Fnews\u002Fmodel-context-protocol",[1526,2004,2005,2008,2011,2014],{},[1543,2006,2007],{},"11",[1543,2009,2010],{},"MCP 文档",[1543,2012,2013],{},"modelcontextprotocol.io",[1543,2015,2016],{},"modelcontextprotocol.io\u002Fdocs\u002Fgetting-started\u002Fintro",[1526,2018,2019,2022,2024,2027],{},[1543,2020,2021],{},"12",[1543,2023,1650],{},[1543,2025,2026],{},"LangChain 官方",[1543,2028,2029],{},"python.langchain.com\u002Fdocs\u002Fintroduction\u002F",[1526,2031,2032,2035,2037,2040],{},[1543,2033,2034],{},"13",[1543,2036,1653],{},[1543,2038,2039],{},"IBM \u002F LangChain 官方",[1543,2041,2042],{},"ibm.com\u002Fthink\u002Ftopics\u002Flanggraph",[1526,2044,2045,2048,2050,2052],{},[1543,2046,2047],{},"14",[1543,2049,1703],{},[1543,2051,1999],{},[1543,2053,2054],{},"anthropic.com\u002Fproduct\u002Fclaude-code",[1526,2056,2057,2060,2063,2066],{},[1543,2058,2059],{},"15",[1543,2061,2062],{},"Claude Code 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