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框架","主流 Agent 框架：LangChain、LlamaIndex、AutoGen、CrewAI",{"path":17,"title":18,"description":19},"\u002Fai\u002Fagent\u002Fhooks","Agent Hooks 与自动化","Claude Agent 的 Hooks 生命周期、事件类型、典型自动化场景",{"path":21,"title":22,"description":23},"\u002Fai\u002Fagent\u002Fintroduction","AI Agent 概述","AI Agent 核心概念：感知、规划、执行、记忆",{"path":25,"title":26,"description":27},"\u002Fai\u002Fagent\u002Fpractice","Agent 实战","AI Agent 实战：构建自主任务执行系统",{"path":29,"title":30,"description":31},"\u002Fai\u002Fagent\u002Fsdk","Claude Agent SDK 开发","使用 Claude Agent SDK 构建自定义 AI Agent：架构、API、生命周期",{"path":33,"title":34,"description":35},"\u002Fai\u002Fagent\u002Fsubagents","Subagents 子代理","用 Subagents 分解复杂任务、并发执行、隔离上下文",{"path":37,"title":38,"description":39},"\u002Fai\u002Fagent\u002Ftool-use","工具调用","AI Agent 工具调用：Function Calling、Tool Use 原理与实践",{"path":41,"title":42,"description":43},"\u002Fai\u002Ffundamentals\u002Fdeep-learning","深度学习入门","深度学习基础知识：前向传播、反向传播、损失函数、优化器",{"path":45,"title":46,"description":47},"\u002Fai\u002Ffundamentals\u002Fml-basics","机器学习基础","机器学习核心概念：监督学习、无监督学习、强化学习",{"path":49,"title":50,"description":51},"\u002Fai\u002Ffundamentals\u002Fneural-networks","神经网络原理","神经网络架构：CNN、RNN、注意力机制",{"path":53,"title":54,"description":55},"\u002Fai\u002Fgetting-started","AI 学习路线","AI 技术学习路线图，从基础到实战的完整指南",{"path":57,"title":58,"description":59},"\u002Fai\u002Fllm\u002Ffine-tuning","模型微调","大模型微调技术：LoRA、QLoRA、全量微调、RLHF",{"path":61,"title":62,"description":63},"\u002Fai\u002Fllm\u002Fintroduction","大模型概述","大语言模型发展历程、核心能力与主流模型对比",{"path":65,"title":66,"description":67},"\u002Fai\u002Fllm\u002Flocal-deploy","本地部署","大模型本地部署：Ollama、vLLM、llama.cpp",{"path":69,"title":70,"description":71},"\u002Fai\u002Fllm\u002Ftransformer","Transformer 架构","Transformer 架构详解：自注意力机制、位置编码、多头注意力",{"path":73,"title":74,"description":75},"\u002Fai\u002Fmcp\u002Fclient","MCP Client 开发","MCP Client 开发指南：连接、调用、集成",{"path":77,"title":78,"description":79},"\u002Fai\u002Fmcp\u002Fdebugging","MCP 调试与排错","MCP Server 开发与集成过程中的常见问题、日志分析、诊断工具",{"path":81,"title":82,"description":83},"\u002Fai\u002Fmcp\u002Fintroduction","MCP 概述","Model Context Protocol 协议概述：架构、核心概念、应用场景",{"path":85,"title":86,"description":87},"\u002Fai\u002Fmcp\u002Fserver","MCP Server 开发","MCP Server 开发指南：资源、工具、提示词的实现",{"path":89,"title":90,"description":91},"\u002Fai\u002Fmcp\u002Ftools","MCP Tools 深入","深入理解 MCP Tools：与 Resources\u002FPrompts 的差异、Schema 设计、Annotations 与权限控制",{"path":93,"title":94,"description":95},"\u002Fai\u002Fprompt\u002Fadvanced","高级 Prompt 模式","高级 Prompt 设计模式：Tree-of-Thought、自我反思、多轮对话策略",{"path":97,"title":98,"description":99},"\u002Fai\u002Fprompt\u002Fbasics","Prompt 基础","Prompt Engineering 入门：基本概念、角色设定、输出格式控制",{"path":101,"title":102,"description":103},"\u002Fai\u002Fprompt\u002Ftechniques","提示词技巧","常用提示词技巧：Few-shot、Chain-of-Thought、ReAct",{"path":105,"title":106,"description":107},"\u002Fai\u002Frag\u002Fembedding","文本嵌入","文本嵌入模型：Embedding 原理、模型选择、相似度计算",{"path":109,"title":110,"description":111},"\u002Fai\u002Frag\u002Fintroduction","RAG 概述","检索增强生成（RAG）架构原理、优势与应用场景",{"path":113,"title":114,"description":115},"\u002Fai\u002Frag\u002Fpractice","RAG 实战","RAG 应用实战：文档问答系统、知识库搭建",{"path":117,"title":118,"description":119},"\u002Fai\u002Frag\u002Fvector-database","向量数据库","主流向量数据库对比：Milvus、Pinecone、Chroma、Weaviate",{"path":121,"title":122,"description":123},"\u002Fai\u002Fskills\u002Fbest-practices","Skill 最佳实践","编写高质量 Skill 的设计原则、常见陷阱与优化技巧",{"path":125,"title":126,"description":127},"\u002Fai\u002Fskills\u002Fcreating","创建自定义 Skill","从零编写一个可被 Agent 自动发现和调用的 Skill",{"path":129,"title":130,"description":131},"\u002Fai\u002Fskills\u002Fintroduction","Agent Skills 概述","Claude Agent Skills 概念、工作原理、与 Tools\u002FMCP 的区别",{"path":133,"title":134,"description":135},"\u002Fgolang\u002Fadvanced\u002Fconcurrency","Go - 并发深入","深入理解 Go 并发编程的核心机制。",{"path":137,"title":138,"description":139},"\u002Fgolang\u002Fadvanced\u002Fgc","Go - 垃圾回收","理解 Go 的垃圾回收机制，掌握 GC 调优方法。",{"path":141,"title":142,"description":143},"\u002Fgolang\u002Fadvanced\u002Fgmp","Go - GMP 调度模型","GMP 是 Go 运行时调度器的核心模型，理解它对于编写高性能 Go 程序至关重要。",{"path":145,"title":146,"description":147},"\u002Fgolang\u002Fadvanced\u002Fgo-concurrency","Go - 并发编程","Go 的并发是其核心特性之一，通过 Goroutine 和 Channel 实现。",{"path":149,"title":150,"description":151},"\u002Fgolang\u002Fadvanced\u002Fmemory","Go - 内存模型","理解 Go 的内存分配机制和内存模型。",{"path":153,"title":154,"description":155},"\u002Fgolang\u002Fadvanced\u002Fprofiling","Go - 性能分析","掌握 Go 的性能分析工具：pprof、trace、benchmark。",{"path":157,"title":158,"description":11},"\u002Fgolang\u002Fcore\u002Fgo-basic","Go - 基础语法",{"path":160,"title":161,"description":11},"\u002Fgolang\u002Fcore\u002Fgo-composite","Go - 复合类型",{"path":163,"title":164,"description":11},"\u002Fgolang\u002Fcore\u002Fgo-control","Go - 流程控制",{"path":166,"title":167,"description":168},"\u002Fgolang\u002Fcore\u002Fgo-error","Go - 错误处理","Go 使用显式的错误返回值来处理错误，而不是异常机制。",{"path":170,"title":171,"description":11},"\u002Fgolang\u002Fcore\u002Fgo-function","Go - 函数",{"path":173,"title":174,"description":11},"\u002Fgolang\u002Fcore\u002Fgo-install","Go - 环境搭建",{"path":176,"title":177,"description":11},"\u002Fgolang\u002Fcore\u002Fgo-interface","Go - 接口",{"path":179,"title":180,"description":181},"\u002Fgolang\u002Fcore\u002Fgo-module","Go - 包管理","Go Modules 是 Go 1.11 引入的官方依赖管理方案，Go 1.16 后成为默认模式。",{"path":183,"title":184,"description":185},"\u002Fgolang\u002Fdistributed\u002Fgrpc","Go - gRPC","gRPC 是 Google 开发的高性能 RPC 框架，使用 Protocol Buffers 作为序列化协议。",{"path":187,"title":188,"description":189},"\u002Fgolang\u002Fdistributed\u002Fmicroservice","Go - 微服务","微服务架构的核心组件：服务发现、负载均衡、熔断降级。",{"path":191,"title":192,"description":193},"\u002Fgolang\u002Fdistributed\u002Fmq","Go - 消息队列","使用 Go 操作 Kafka 和 RabbitMQ。",{"path":195,"title":196,"description":197},"\u002Fgolang\u002Fdistributed\u002Fredis","Go - Redis","使用 go-redis 操作 Redis，实现缓存、分布式锁等功能。",{"path":199,"title":200,"description":201},"\u002Fgolang\u002Fengineering\u002Fconfig","Go - 配置管理","使用 viper 进行配置管理，支持多种配置格式和配置中心。",{"path":203,"title":204,"description":205},"\u002Fgolang\u002Fengineering\u002Fdocker","Go - Docker 部署","使用 Docker 容器化部署 Go 应用。",{"path":207,"title":208,"description":209},"\u002Fgolang\u002Fengineering\u002Fkubernetes","Go - Kubernetes 部署","在 Kubernetes 上部署和管理 Go 应用。",{"path":211,"title":212,"description":213},"\u002Fgolang\u002Fengineering\u002Flogging","Go - 日志系统","使用 zap 和 logrus 构建高性能结构化日志系统。",{"path":215,"title":216,"description":217},"\u002Fgolang\u002Fengineering\u002Ftesting","Go - 单元测试","Go 内置了强大的测试框架，掌握测试是编写高质量代码的基础。",{"path":219,"title":220,"description":221},"\u002Fgolang\u002Fstdlib\u002Fbufio","bufio","在 Go 语言中，bufio 包提供了带缓冲的 I\u002FO 操作，能够提高读写性能。以下是一些常用的 bufio 包 API 及其详细说明：",{"path":223,"title":224,"description":225},"\u002Fgolang\u002Fstdlib\u002Fcontainer","container","在Go语言标准库中，container 包提供了几种常用的数据结构实现，这些数据结构对于高效地管理和操作数据非常有用。以下是 container 包中主要的数据结构：",{"path":227,"title":228,"description":229},"\u002Fgolang\u002Fstdlib\u002Fcrypto","crypto","在 Go 语言中，crypto 包提供了一组用于加密和解密的功能。以下是一些常用的 crypto 包及其子包的 API 及其详细说明：",{"path":231,"title":232,"description":233},"\u002Fgolang\u002Fstdlib\u002Fencoding-csv","encoding\u002Fcsv","在 Go 语言中，encoding\u002Fcsv 包提供了对 CSV（逗号分隔值）文件进行读写的功能。以下是一些常用的 encoding\u002Fcsv 包的 API 及其详细说明：",{"path":235,"title":236,"description":237},"\u002Fgolang\u002Fstdlib\u002Fencoding-json","encoding\u002Fjson","在 Go 语言中，encoding\u002Fjson 包提供了对 JSON 数据进行编码和解码的功能。以下是一些常用的 encoding\u002Fjson 包的 API 及其详细说明：",{"path":239,"title":240,"description":241},"\u002Fgolang\u002Fstdlib\u002Fencoding-xml","encoding\u002Fxml","在 Go 语言中，encoding\u002Fxml 包提供了对 XML 数据进行编码和解码的功能。以下是一些常用的 encoding\u002Fxml 包的 API 及其详细说明：",{"path":243,"title":244,"description":245},"\u002Fgolang\u002Fstdlib\u002Fflag","flag","在Go语言中，flag 包是用于处理命令行参数的标准库，它提供了一种简单而直接的方式来解析和使用命令行参数。下面是关于 flag 包的一些基本介绍和常用功能：",{"path":247,"title":248,"description":249},"\u002Fgolang\u002Fstdlib\u002Ffmt","fmt","在 Go 语言的标准库中，fmt 包是非常重要的，它提供了处理格式化输入和输出的基本工具。以下是一些 fmt 包内常用的API：",{"path":251,"title":252,"description":253},"\u002Fgolang\u002Fstdlib\u002Fhttp","net\u002Fhttp","在 Go 语言中，net\u002Fhttp 包提供了用于构建 HTTP 客户端和服务器的强大工具。以下是一些常用的 net\u002Fhttp 包的 API 及其详细说明：",{"path":255,"title":256,"description":257},"\u002Fgolang\u002Fstdlib\u002Fio","io","在 Go 语言中，io 包提供了基本的输入输出功能。以下是一些常用的 io 包的 API 及其详细说明：",{"path":259,"title":260,"description":261},"\u002Fgolang\u002Fstdlib\u002Flog","log","在 Go 语言中，log 包提供了简单的日志记录功能。以下是一些常用的 log 包的 API 及其详细说明：",{"path":263,"title":264,"description":265},"\u002Fgolang\u002Fstdlib\u002Fmath","math","在 Go 语言中，math 包提供了基本的数学函数和常量。以下是一些常用的 math 包的 API 及其详细说明：",{"path":267,"title":268,"description":11},"\u002Fgolang\u002Fstdlib\u002Fnet","net",{"path":270,"title":271,"description":272},"\u002Fgolang\u002Fstdlib\u002Fos","os","在Go语言中，os 包是一个非常重要且常用的标准库，它提供了与操作系统交互的功能，包括文件操作、环境变量管理、进程管理等。下面是一些 os 包中常用的功能和API：",{"path":274,"title":275,"description":276},"\u002Fgolang\u002Fstdlib\u002Fsort","order","在 Go 语言中，sort 包提供了对切片和用户定义的集合进行排序的函数。它实现了常见的排序算法，如快速排序（Quicksort）和堆排序（Heapsort），并且为自定义集合提供了接口，使得用户可以根据特定的需求进行排序。",{"path":278,"title":279,"description":280},"\u002Fgolang\u002Fstdlib\u002Fstrconv","strconv","在 Go 语言中，strconv 包提供了字符串和基本数据类型之间的转换函数，例如将整数转换为字符串、字符串转换为整数，以及其他类型之间的转换。这些功能非常有用，特别是在处理用户输入或从外部数据源读取数据时。",{"path":282,"title":283,"description":284},"\u002Fgolang\u002Fstdlib\u002Ftime","time","在 Go 语言中，time 包提供了处理时间和日期的功能。以下是一些常用的 time 包的 API 及其详细说明：",{"path":286,"title":287,"description":11},"\u002Fgolang\u002Fweb\u002Fgin\u002Ferror","Gin - 错误处理",{"path":289,"title":290,"description":11},"\u002Fgolang\u002Fweb\u002Fgin\u002Ffile","Gin - 文件处理",{"path":292,"title":293,"description":11},"\u002Fgolang\u002Fweb\u002Fgin\u002Fmiddleware","Gin - 中间件",{"path":295,"title":296,"description":297},"\u002Fgolang\u002Fweb\u002Fgin\u002Fquickstart","Gin - 快速开始","Gin 是目前最流行的 Go Web 框架，以高性能和简洁 API 著称。",{"path":299,"title":300,"description":11},"\u002Fgolang\u002Fweb\u002Fgin\u002Frequest","Gin - 请求处理",{"path":302,"title":303,"description":11},"\u002Fgolang\u002Fweb\u002Fgin\u002Fresponse","Gin - 响应处理",{"path":305,"title":306,"description":11},"\u002Fgolang\u002Fweb\u002Fgin\u002Frouter","Gin - 路由",{"path":308,"title":309,"description":11},"\u002Fgolang\u002Fweb\u002Fgin\u002Fvalidation","Gin - 参数校验",{"path":311,"title":312,"description":11},"\u002Fgolang\u002Fweb\u002Fgorm\u002Fassociation","GORM - 关联关系",{"path":314,"title":315,"description":11},"\u002Fgolang\u002Fweb\u002Fgorm\u002Fcrud","GORM - CRUD 操作",{"path":317,"title":318,"description":11},"\u002Fgolang\u002Fweb\u002Fgorm\u002Fmodel","GORM - 模型定义",{"path":320,"title":321,"description":11},"\u002Fgolang\u002Fweb\u002Fgorm\u002Fperformance","GORM - 日志与性能",{"path":323,"title":324,"description":11},"\u002Fgolang\u002Fweb\u002Fgorm\u002Fquery","GORM - 高级查询",{"path":326,"title":327,"description":328},"\u002Fgolang\u002Fweb\u002Fgorm\u002Fquickstart","GORM - 快速开始","GORM 是 Go 语言最流行的 ORM 库，功能强大，使用简单。",{"path":330,"title":331,"description":11},"\u002Fgolang\u002Fweb\u002Fgorm\u002Ftransaction","GORM - 事务与 Hook",{"path":333,"title":334,"description":335},"\u002Finterview\u002Fbasic","计算机基础面经","本章节汇总了面试中常见的通用技术概念，不局限于特定语言或数据库，是考察技术内功的关键考点。",{"path":337,"title":338,"description":339},"\u002Finterview\u002Fgolang","Golang 面试题","Go 语言面试高频考点，覆盖基础语法、数据结构、并发编程、内存管理、GC、调度器等核心知识。",{"path":341,"title":342,"description":343},"\u002Finterview\u002Fk8s","Kubernetes 面试题","Kubernetes（K8s）面试高频考点，覆盖架构原理、核心资源、网络存储、调度策略、运维监控等核心知识。",{"path":345,"title":346,"description":347},"\u002Finterview\u002Fmysql","MySQL 面试题","MySQL 数据库面试高频考点，覆盖索引、事务、锁、优化、主从复制等核心知识。",{"path":349,"title":350,"description":351},"\u002Finterview\u002Fredis","Redis 面试题","Redis 面试高频考点，覆盖数据结构、持久化、集群、缓存一致性、性能优化等核心知识。",{"path":353,"title":354,"description":355},"\u002Finterview\u002Frocketmq","RocketMQ 面试题","RocketMQ 面试高频考点，覆盖消息模型、可靠性、顺序消息、事务消息、存储与高可用等核心知识。",{"path":357,"title":358,"description":11},"\u002Fother\u002Fjava\u002Fcollection\u002Flist-arraylist","List - ArrayList 源码解析",{"path":360,"title":361,"description":11},"\u002Fother\u002Fjava\u002Fcollection\u002Flist-linkedlist","List - LinkedList 源码解析",{"path":363,"title":364,"description":11},"\u002Fother\u002Fjava\u002Fcollection\u002Flist-stack","List - Satck源码解析",{"path":366,"title":367,"description":11},"\u002Fother\u002Fjava\u002Fcollection\u002Flist-vectore","List - Vector 源码解析",{"path":369,"title":370,"description":11},"\u002Fother\u002Fjava\u002Fcollection\u002Fmap-hashmap","Map - HashMap 源码解析",{"path":372,"title":373,"description":11},"\u002Fother\u002Fjava\u002Fcollection\u002Fmap-linkedhashmap","Map - LinkedHashMap 源码解析",{"path":375,"title":376,"description":11},"\u002Fother\u002Fjava\u002Fcollection\u002Fmap-treemap","Map - TreeMap 源码解析",{"path":378,"title":379,"description":11},"\u002Fother\u002Fjava\u002Fcollection\u002Fqueue-deque","Queue - Deque 接口解析",{"path":381,"title":382,"description":11},"\u002Fother\u002Fjava\u002Fcollection\u002Fqueue-queue","Queue - Queue 接口解析",{"path":384,"title":385,"description":11},"\u002Fother\u002Fjava\u002Fcollection\u002Fset-hashset","Set - HashSet源码解析",{"path":387,"title":388,"description":11},"\u002Fother\u002Fjava\u002Fcollection\u002Fset-linkedhashset","Set - LinkedHashSet 源码解析",{"path":390,"title":391,"description":392},"\u002Fother\u002Fjava\u002Fcollection\u002Fset-treeset","Set - TreeSet源码解析","TreeSet 是一个 Set 集合接口的实现类，与 HashSet 类似，其底层也是通过维护了一个 TreeMap 对象来封装了一些实现方法，故本篇不再对 TreeSet 的底层原理进行详细说明，仅对常用 API 做简单介绍，如需了解 TreeMap 的底层实现原理，请移步 Map - HashMap 源码解析",{"path":394,"title":395,"description":11},"\u002Fother\u002Fjava\u002Fcore\u002Fannotation","Java核心 - 注解",{"path":397,"title":398,"description":11},"\u002Fother\u002Fjava\u002Fcore\u002Fbasic-grammar","Java核心 - 基础语法",{"path":400,"title":401,"description":11},"\u002Fother\u002Fjava\u002Fcore\u002Fclass-and-object","Java核心 - 面向对象",{"path":403,"title":404,"description":11},"\u002Fother\u002Fjava\u002Fcore\u002Fcommon-classes","Java核心 - 常用类",{"path":406,"title":407,"description":11},"\u002Fother\u002Fjava\u002Fcore\u002Fexception","Java核心 - 异常处理",{"path":409,"title":410,"description":11},"\u002Fother\u002Fjava\u002Fcore\u002Fgenerics","Java核心 - 泛型",{"path":412,"title":413,"description":11},"\u002Fother\u002Fjava\u002Fcore\u002Fjdk-env-path","Java核心 - 环境搭建",{"path":415,"title":416,"description":11},"\u002Fother\u002Fjava\u002Fcore\u002Freflection","Java核心 - 反射",{"path":418,"title":419,"description":11},"\u002Fother\u002Fjava\u002Fcore\u002Fstring","Java核心 - String 字符串",{"path":421,"title":422,"description":423},"\u002Fother\u002Fjava\u002Fio\u002Fbuffer-stream","Java IO - 缓冲流","缓冲流是对基本流的包装，通过内置缓冲区减少系统调用次数，大幅提升读写效率。",{"path":425,"title":426,"description":427},"\u002Fother\u002Fjava\u002Fio\u002Fbyte-stream","Java IO - 字节流","字节流是 Java IO 中最基本的流类型，以字节（byte）为单位进行数据读写，可以处理任意类型的文件。",{"path":429,"title":430,"description":431},"\u002Fother\u002Fjava\u002Fio\u002Fchar-stream","Java IO - 字符流","字符流以字符为单位进行读写，专门用于处理文本文件。相比字节流，字符流能够正确处理字符编码，避免中文乱码问题。",{"path":433,"title":434,"description":11},"\u002Fother\u002Fjava\u002Fio\u002Ffile","Java IO - File 类",{"path":436,"title":437,"description":11},"\u002Fother\u002Fjava\u002Fio\u002Fio-stream-system","Java IO - IO流概述",{"path":439,"title":440,"description":441},"\u002Fother\u002Fjava\u002Fio\u002Fnio","Java IO - NIO","NIO（New IO）是 JDK 1.4 引入的新 IO 模型，提供了更高效的 IO 操作方式，支持非阻塞 IO 和多路复用。",{"path":443,"title":444,"description":445},"\u002Fother\u002Fjava\u002Fjvm\u002Fclass-loading","类加载机制","类加载机制是 JVM 将 .class 文件加载到内存，并对数据进行校验、转换解析和初始化，最终形成可被 JVM 直接使用的 Java 类型的过程。",{"path":447,"title":448,"description":449},"\u002Fother\u002Fjava\u002Fjvm\u002Fgarbage-collection","垃圾回收","垃圾回收（Garbage Collection，GC）是 JVM 自动管理内存的机制，负责回收不再使用的对象所占用的内存。",{"path":451,"title":452,"description":453},"\u002Fother\u002Fjava\u002Fjvm\u002Fjvm-memory","JVM 内存结构","JVM 在执行 Java 程序时，会把它管理的内存划分为若干个不同的数据区域。这些区域有各自的用途、创建和销毁时间。",{"path":455,"title":456,"description":457},"\u002Fother\u002Fjava\u002Fjvm\u002Fjvm-tuning","JVM 调优","JVM 调优是优化 Java 应用性能的重要手段，主要包括参数配置、性能监控和问题排查。",{"path":459,"title":460,"description":461},"\u002Fother\u002Fjava\u002Fthread\u002Fatomic","原子类","Java 原子类（Atomic Classes）提供了一种无锁的线程安全方式，基于 CAS（Compare-And-Swap）操作实现。",{"path":463,"title":464,"description":465},"\u002Fother\u002Fjava\u002Fthread\u002Fcompletable-future","CompletableFuture","CompletableFuture 是 JDK 8 引入的异步编程工具，实现了 Future 和 CompletionStage 接口，支持函数式编程和链式调用。",{"path":467,"title":468,"description":469},"\u002Fother\u002Fjava\u002Fthread\u002Fconcurrent-collections","并发集合","Java 并发包提供了多种线程安全的集合类，用于替代传统的同步集合（如 Collections.synchronizedList）。",{"path":471,"title":472,"description":473},"\u002Fother\u002Fjava\u002Fthread\u002Fconcurrent-utils","并发工具类","Java 并发包提供了多种实用的并发工具类，用于控制线程之间的协调与同步。",{"path":475,"title":476,"description":11},"\u002Fother\u002Fjava\u002Fthread\u002Fsynchronized-lock","同步机制",{"path":478,"title":479,"description":11},"\u002Fother\u002Fjava\u002Fthread\u002Fthread-basic","线程基础",{"path":481,"title":482,"description":11},"\u002Fother\u002Fjava\u002Fthread\u002Fthread-pool","线程池",{"path":484,"title":485,"description":486},"\u002Fother\u002Fspring-series\u002Fspring\u002Fannotations-beans","Spring - 基于注解管理Bean","从 Java 5 开始，Java 增加了对注解（Annotation）的支持，它是代码中的一种特殊标记，可以在编译、类加载和运行时被读取，执行相应的处理。开发人员可以通过注解在不改变原有代码和逻辑的情况下，在源代码中嵌入补充信息。",{"path":488,"title":489,"description":490},"\u002Fother\u002Fspring-series\u002Fspring\u002Fimplement-ioc","Spring - 原理手写IoC","Spring 框架的 IOC 是基于 Java 反射机制实现的，在学习手写 IoC 之前，你需要具备一定的 Java 反射相关的知识，参考本站内的 Java 教程。",{"path":492,"title":493,"description":11},"\u002Fother\u002Fspring-series\u002Fspring\u002Fintroduction-case","Spring - 入门案例",{"path":495,"title":496,"description":11},"\u002Fother\u002Fspring-series\u002Fspring\u002Fspring-aop","Spring - 面向切面AOP",{"path":498,"title":499,"description":11},"\u002Fother\u002Fspring-series\u002Fspring\u002Fspring-aot","Spring - AOT提前编译",{"path":501,"title":502,"description":503},"\u002Fother\u002Fspring-series\u002Fspring\u002Fspring-data-validation","Spring - 数据校验","在开发中，我们经常遇到参数校验的需求，比如用户注册的时候，要校验用户名不能为空、用户名长度不超过20个字符、手机号是合法的手机号格式等等。如果使用普通方式，我们会把校验的代码和真正的业务处理逻辑耦合在一起，而且如果未来要新增一种校验逻辑也需要在修改多个地方。而spring validation允许通过注解的方式来定义对象校验规则，把校验和业务逻辑分离开，让代码编写更加方便。Spring Validation其实就是对Hibernate Validator进一步的封装，方便在Spring中使用。",{"path":505,"title":506,"description":11},"\u002Fother\u002Fspring-series\u002Fspring\u002Fspring-i18n","Spring - 国际化i18n",{"path":508,"title":509,"description":510},"\u002Fother\u002Fspring-series\u002Fspring\u002Fspring-ioc","Spring - IOC容器","IoC 是 Inversion of Control 的简写，译为“控制反转”，它不是一门技术，而是一种设计思想，是一个重要的面向对象编程法则，能够指导我们如何设计出松耦合、更优良的程序。",{"path":512,"title":513,"description":514},"\u002Fother\u002Fspring-series\u002Fspring\u002Fspring-junit","Spring - 单元测试JUnit","在之前的测试方法中，几乎都能看到以下的两行代码：",{"path":516,"title":517,"description":11},"\u002Fother\u002Fspring-series\u002Fspring\u002Fspring-resources","Spring - 资源操作",{"path":519,"title":520,"description":11},"\u002Fother\u002Fspring-series\u002Fspring\u002Fspring-summarize","Spring - Spring概述",{"path":522,"title":523,"description":11},"\u002Fother\u002Fspring-series\u002Fspring\u002Fspring-transaction","Spring - 事务",{"path":525,"title":526,"description":11},"\u002Fother\u002Fspring-series\u002Fspring\u002Fxml-beans","Spring - 基于XML管理Bean",{"path":528,"title":529,"description":11},"\u002Fother\u002Fspring-series\u002Fspringboot\u002Fspringboot-config","SpringBoot - 配置详解",{"path":531,"title":532,"description":11},"\u002Fother\u002Fspring-series\u002Fspringboot\u002Fspringboot-data","SpringBoot - 数据访问",{"path":534,"title":535,"description":11},"\u002Fother\u002Fspring-series\u002Fspringboot\u002Fspringboot-quickstart","SpringBoot - 快速入门",{"path":537,"title":538,"description":11},"\u002Fother\u002Fspring-series\u002Fspringboot\u002Fspringboot-web","SpringBoot - Web 开发",{"path":540,"title":541,"description":11},"\u002Fother\u002Fspring-series\u002Fspringcloud\u002Fspringcloud-config","SpringCloud - 配置中心",{"path":543,"title":544,"description":11},"\u002Fother\u002Fspring-series\u002Fspringcloud\u002Fspringcloud-discovery","SpringCloud - 服务注册与发现",{"path":546,"title":547,"description":11},"\u002Fother\u002Fspring-series\u002Fspringcloud\u002Fspringcloud-feign","SpringCloud - 服务调用",{"path":549,"title":550,"description":11},"\u002Fother\u002Fspring-series\u002Fspringcloud\u002Fspringcloud-gateway","SpringCloud - 服务网关",{"path":552,"title":553,"description":11},"\u002Fother\u002Fspring-series\u002Fspringcloud\u002Fspringcloud-introduction","SpringCloud - 微服务概述",{"path":555,"title":556,"description":11},"\u002Fother\u002Fspring-series\u002Fspringcloud\u002Fspringcloud-sentinel","SpringCloud - 服务保护",{"path":558,"title":559,"description":11},"\u002Fother\u002Fspring-series\u002Fspringmvc\u002Fspringmvc-databind","SpringMVC - 数据绑定与转换",{"path":561,"title":562,"description":11},"\u002Fother\u002Fspring-series\u002Fspringmvc\u002Fspringmvc-exception","SpringMVC - 异常处理",{"path":564,"title":565,"description":11},"\u002Fother\u002Fspring-series\u002Fspringmvc\u002Fspringmvc-interceptor","SpringMVC - 拦截器",{"path":567,"title":568,"description":11},"\u002Fother\u002Fspring-series\u002Fspringmvc\u002Fspringmvc-introduction","SpringMVC - 简介与环境搭建",{"path":570,"title":571,"description":11},"\u002Fother\u002Fspring-series\u002Fspringmvc\u002Fspringmvc-request","SpringMVC - 请求处理",{"path":573,"title":574,"description":11},"\u002Fother\u002Fspring-series\u002Fspringmvc\u002Fspringmvc-response","SpringMVC - 响应处理",{"path":576,"title":577,"description":578},"\u002Fproject\u002Frocket-leaf\u002Farchitecture","项目架构","Rocket-Leaf 的目录结构、模块划分、数据流向，以及各层之间的依赖关系。",{"path":580,"title":581,"description":582},"\u002Fproject\u002Frocket-leaf\u002Fbackend-layers","后端分层设计","Rocket-Leaf 的 model \u002F rocketmq \u002F service 三层结构，以及服务之间的依赖关系与设计取舍。",{"path":584,"title":585,"description":586},"\u002Fproject\u002Frocket-leaf\u002Fclient-manager","RocketMQ 客户端管理器","AdminClientManager 的多客户端池、默认连接懒加载、自动重连重试的设计与实现。",{"path":588,"title":589,"description":590},"\u002Fproject\u002Frocket-leaf\u002Fencryption","连接信息加密存储","AES-256-GCM + SHA-256 字段级派生密钥的实现，以及如何在不破坏兼容性的前提下为历史明文数据做透明迁移。",{"path":592,"title":593,"description":594},"\u002Fproject\u002Frocket-leaf\u002Ffrontend","前端结构与类型绑定","React + Vite 目录组织、自动生成的 Wails 绑定、api 薄封装与自定义 hooks 的职责划分。",{"path":596,"title":597,"description":598},"\u002Fproject\u002Frocket-leaf","项目简介","Rocket-Leaf 是一款基于 Wails v3 构建的跨平台 RocketMQ 桌面管理客户端，Go 后端 + React 前端。本文档系列拆解它的架构与关键实现。",{"path":600,"title":601,"description":602},"\u002Fproject\u002Frocket-leaf\u002Fwails-v3","Wails v3 入门","Wails v3 的核心概念、Service 绑定机制，以及 Rocket-Leaf 是如何用它把 Go 后端和 React 前端打通的。",{"path":604,"title":605,"description":11},"\u002Ftutorials\u002Fcloud\u002Fdocker\u002Fdocker-basic","Docker - 入门基础",{"path":607,"title":608,"description":609},"\u002Ftutorials\u002Fcloud\u002Fdocker\u002Fdocker-compose","Docker - Compose","在部署应用时，常常使用到不止一个容器，那么在部署容器的时候就需要一个一个进行部署，这样的部署过程也相对来说比较繁琐复杂，也容易出问题，那么有没有一种更为简单的方法呢？",{"path":611,"title":612,"description":613},"\u002Ftutorials\u002Fcloud\u002Fdocker\u002Fdocker-container-connection","Docker - 容器互联","在上一个章节中我们学习了 Docker 容器的端口映射，可以将 Docker 容器和本地以及网络中的端口进行连接起来。",{"path":615,"title":616,"description":11},"\u002Ftutorials\u002Fcloud\u002Fdocker\u002Fdocker-dockerfile","Docker - Dockerfile",{"path":618,"title":619,"description":11},"\u002Ftutorials\u002Fcloud\u002Fdocker\u002Fdocker-helloworld","Docker - HelloWorld",{"path":621,"title":622,"description":11},"\u002Ftutorials\u002Fcloud\u002Fdocker\u002Fdocker-install","Docker - 安装",{"path":624,"title":625,"description":11},"\u002Ftutorials\u002Fcloud\u002Fdocker\u002Fdocker-introduce","Docker - 简介",{"path":627,"title":628,"description":11},"\u002Ftutorials\u002Fcloud\u002Fdocker\u002Fdocker-object","Docker - 镜像、容器、仓库",{"path":630,"title":631,"description":632},"\u002Ftutorials\u002Fcloud\u002Fdocker\u002Fdocker-warehouse","Docker - 仓库管理","仓库是集中存放资源的地方，代码仓库是存放代码的，那么Docker 中的仓库就是存放 Docker 镜像的。",{"path":634,"title":635,"description":636},"\u002Ftutorials\u002Fcloud\u002Fdocker\u002Fdocker-web-containers","Docker - WEB应用实例","在之前的章节中，仅对普通容器进行了演示，但在实际中常常使用到 Docker 容器中的 WEB 应用程序。",{"path":638,"title":639,"description":11},"\u002Ftutorials\u002Fcloud\u002Fkubernetes\u002Fk8s-config","Kubernetes - ConfigMap 与 Secret",{"path":641,"title":642,"description":11},"\u002Ftutorials\u002Fcloud\u002Fkubernetes\u002Fk8s-helm","Kubernetes - Helm 包管理",{"path":644,"title":645,"description":11},"\u002Ftutorials\u002Fcloud\u002Fkubernetes\u002Fk8s-install","Kubernetes - 集群安装",{"path":647,"title":648,"description":11},"\u002Ftutorials\u002Fcloud\u002Fkubernetes\u002Fk8s-introduction","Kubernetes - 简介与架构",{"path":650,"title":651,"description":11},"\u002Ftutorials\u002Fcloud\u002Fkubernetes\u002Fk8s-kubectl","Kubernetes - kubectl 命令行工具",{"path":653,"title":654,"description":11},"\u002Ftutorials\u002Fcloud\u002Fkubernetes\u002Fk8s-monitoring","Kubernetes - 监控与日志",{"path":656,"title":657,"description":11},"\u002Ftutorials\u002Fcloud\u002Fkubernetes\u002Fk8s-network-security","Kubernetes - 网络与安全",{"path":659,"title":660,"description":11},"\u002Ftutorials\u002Fcloud\u002Fkubernetes\u002Fk8s-service","Kubernetes - Service 与 Ingress",{"path":662,"title":663,"description":11},"\u002Ftutorials\u002Fcloud\u002Fkubernetes\u002Fk8s-storage","Kubernetes - 持久化存储",{"path":665,"title":666,"description":11},"\u002Ftutorials\u002Fcloud\u002Fkubernetes\u002Fk8s-workload","Kubernetes - 工作负载资源",{"path":668,"title":669,"description":11},"\u002Ftutorials\u002Fcloud\u002Flinux\u002Flinux-bash","Linux - Bash 基础语法",{"path":671,"title":672,"description":11},"\u002Ftutorials\u002Fcloud\u002Flinux\u002Flinux-file-directory","Linux - 文件与目录操作",{"path":674,"title":675,"description":11},"\u002Ftutorials\u002Fcloud\u002Flinux\u002Flinux-network","Linux - 网络配置",{"path":677,"title":678,"description":11},"\u002Ftutorials\u002Fcloud\u002Flinux\u002Flinux-package","Linux - 软件包管理",{"path":680,"title":681,"description":11},"\u002Ftutorials\u002Fcloud\u002Flinux\u002Flinux-process","Linux - 进程管理",{"path":683,"title":684,"description":11},"\u002Ftutorials\u002Fcloud\u002Flinux\u002Flinux-scripts","Linux - 常用脚本示例",{"path":686,"title":687,"description":11},"\u002Ftutorials\u002Fcloud\u002Flinux\u002Flinux-service","Linux - 服务管理",{"path":689,"title":690,"description":11},"\u002Ftutorials\u002Fcloud\u002Flinux\u002Flinux-user-permission","Linux - 用户与权限管理",{"path":692,"title":693,"description":11},"\u002Ftutorials\u002Fcloud\u002Fnginx\u002Fnginx-https","Nginx - HTTPS 配置",{"path":695,"title":696,"description":11},"\u002Ftutorials\u002Fcloud\u002Fnginx\u002Fnginx-install","Nginx - 安装与配置",{"path":698,"title":699,"description":11},"\u002Ftutorials\u002Fcloud\u002Fnginx\u002Fnginx-loadbalance","Nginx - 负载均衡",{"path":701,"title":702,"description":11},"\u002Ftutorials\u002Fcloud\u002Fnginx\u002Fnginx-optimization","Nginx - 性能优化",{"path":704,"title":705,"description":11},"\u002Ftutorials\u002Fcloud\u002Fnginx\u002Fnginx-proxy","Nginx - 反向代理",{"path":707,"title":708,"description":11},"\u002Ftutorials\u002Fcloud\u002Fnginx\u002Fnginx-static","Nginx - 静态资源服务",{"path":710,"title":711,"description":11},"\u002Ftutorials\u002Fcloud\u002Fnginx\u002Fnginx-vhost","Nginx - 虚拟主机配置",{"path":713,"title":714,"description":715},"\u002Ftutorials\u002Fdatabase\u002Fmysql\u002Fmysql-architecture","MySQL 高可用架构","主从复制、读写分离、分库分表。",{"path":717,"title":718,"description":719},"\u002Ftutorials\u002Fdatabase\u002Fmysql\u002Fmysql-index","MySQL 索引","索引是帮助 MySQL 高效获取数据的有序数据结构。",{"path":721,"title":722,"description":723},"\u002Ftutorials\u002Fdatabase\u002Fmysql\u002Fmysql-lock","MySQL 锁","锁用于解决并发访问时的数据一致性问题。",{"path":725,"title":726,"description":727},"\u002Ftutorials\u002Fdatabase\u002Fmysql\u002Fmysql-optimize","MySQL 性能优化","SQL 优化是后端开发必备技能。",{"path":729,"title":730,"description":731},"\u002Ftutorials\u002Fdatabase\u002Fmysql\u002Fmysql-transaction","MySQL 事务","事务是一组不可分割的操作，要么全部成功，要么全部失败。",{"path":733,"title":734,"description":735},"\u002Ftutorials\u002Fdatabase\u002Fmysql\u002Fsql-advanced","SQL 进阶","多表查询、子查询、函数、视图、存储过程。",{"path":737,"title":738,"description":739},"\u002Ftutorials\u002Fdatabase\u002Fmysql\u002Fsql-basic","SQL 基础","SQL（Structured Query Language）是操作关系型数据库的标准语言。",{"path":741,"title":742,"description":743},"\u002Ftutorials\u002Fdatabase\u002Fredis\u002Fredis-advanced","Redis 进阶功能","事务、发布订阅、Lua 脚本、Pipeline。",{"path":745,"title":746,"description":747},"\u002Ftutorials\u002Fdatabase\u002Fredis\u002Fredis-basic","Redis 基础","Redis 安装配置与基本命令。",{"path":749,"title":750,"description":751},"\u002Ftutorials\u002Fdatabase\u002Fredis\u002Fredis-cluster","Redis 高可用","主从复制、哨兵、Cluster 集群。",{"path":753,"title":754,"description":755},"\u002Ftutorials\u002Fdatabase\u002Fredis\u002Fredis-datatype","Redis 数据类型","Redis 5 种基本数据类型 + 4 种特殊类型。",{"path":757,"title":758,"description":759},"\u002Ftutorials\u002Fdatabase\u002Fredis\u002Fredis-optimize","Redis 性能优化","内存优化、缓存问题、最佳实践。",{"path":761,"title":762,"description":763},"\u002Ftutorials\u002Fdatabase\u002Fredis\u002Fredis-persistence","Redis 持久化","Redis 提供 RDB 和 AOF 两种持久化方式。",{"path":765,"title":766,"description":767},"\u002Ftutorials\u002Fdatabase\u002Fredis\u002Fredis-principle","Redis 底层原理","数据结构、线程模型、网络模型。",{"path":769,"title":770,"description":771},"\u002Ftutorials\u002Fdev-idea\u002Fdesign-patterns\u002Fbehaiver-patterns\u002Fobserver-pattern","观察者模式","观察者模式属于行为型模式，定义了对象之间的一对多的依赖关系，在这种模式中，当一个对象的状态发生变化时，所有依赖于它的对象都会得到通知，并且执行相关操作。观察者模式又被成为“发布—订阅模式”，即发布者发生改变后，会通知所有订阅者。",{"path":773,"title":774,"description":11},"\u002Ftutorials\u002Fdev-idea\u002Fdesign-patterns\u002Fcreate-patterns\u002Ffactory-pattern","工厂模式",{"path":776,"title":777,"description":778},"\u002Ftutorials\u002Fdev-idea\u002Fdesign-patterns\u002Fcreate-patterns\u002Fsingleton-pattern","单例模式","单例模式是最常用的设计模式之一，他可以保证在整个应用中，某个类只存在一个实例化对象，即全局使用到该类的只有一个对象，这种模式在需要限制某些类的实例数量时非常有用，通常全局只需要一个该对象即可，如一些配置文件映射对象、数据库连接对象等。",{"path":780,"title":781,"description":782},"\u002Ftutorials\u002Fdev-idea\u002Fdesign-patterns\u002Fstructural-patterns\u002Fadapter-pattern","适配器模式","适配器模式是一种结构型模式，可以将一个类的接口转换成客户端所期望的另一种接口，适配器模式可以帮助开发人员在不修改现有代码的情况下，将不兼容的类组合在一起。",{"path":784,"title":785,"description":786},"\u002Ftutorials\u002Fdev-tools\u002Fgit\u002Fgit-basic-operations","Git 创建版本库","在 Git 上创建版本库有两种方式，一种是直接拷贝远程 Git 仓库到本地，另外一种是我们自己创建本地的版本库。",{"path":788,"title":789,"description":11},"\u002Ftutorials\u002Fdev-tools\u002Fgit\u002Fgit-branch-manage","Git 分支管理",{"path":791,"title":792,"description":11},"\u002Ftutorials\u002Fdev-tools\u002Fgit\u002Fgit-content-operations","Git 仓库内容操作",{"path":794,"title":795,"description":11},"\u002Ftutorials\u002Fdev-tools\u002Fgit\u002Fgit-introduce-install","Git 介绍和安装",{"path":797,"title":798,"description":11},"\u002Ftutorials\u002Fdev-tools\u002Fgit\u002Fgit-remote-manage","Git 远程管理",{"path":800,"title":801,"description":11},"\u002Ftutorials\u002Fdev-tools\u002Fgit\u002Fgit-workspace-index-repo","Git 工作原理",{"path":803,"title":804,"description":11},"\u002Ftutorials\u002Fdev-tools\u002Fhomebrew","HomeBrew 教程",{"path":806,"title":807,"description":11},"\u002Ftutorials\u002Fdev-tools\u002Fidea\u002Fshortcuts","快捷键",{"path":809,"title":810,"description":11},"\u002Ftutorials\u002Fdev-tools\u002Fmaven\u002Fintroduce-install-config","Maven - 介绍、安装、配置",{"path":812,"title":813,"description":11},"\u002Ftutorials\u002Ffront-end\u002Fvue3\u002Fbasic-knowledge","2. 基础知识",{"path":815,"title":816,"description":817},"\u002Ftutorials\u002Ffront-end\u002Fvue3\u002Fcomponent-communication","9. 组件通信","在前面的章节内，介绍了 Vue 中最核心的内容——组件的介绍和使用，和 Java 等编程语言相反，组件并不近似于这些变成语言中的类，类可以通过类或者其实例化的对象来相互交互，但 Vue 组件之间的作用域是相互独立的，这就意味着不同组件之间的数据无法相互引用。",{"path":819,"title":820,"description":821},"\u002Ftutorials\u002Ffront-end\u002Fvue3\u002Fcomputed","4. 计算属性","虽然直接在模板中使用表达式方便，但是如果在模板中添加很多逻辑，会让模板变的臃肿且难维护，耦合度较高。有没有一种简单的方式来实现呢？答案是有的。",{"path":823,"title":824,"description":11},"\u002Ftutorials\u002Ffront-end\u002Fvue3\u002Fcreate-vue-project","1. 环境搭建及安装",{"path":826,"title":827,"description":828},"\u002Ftutorials\u002Ffront-end\u002Fvue3\u002Flife-cycle","6. 生命周期","生命周期是指组件从创建、挂载、更新到销毁的整个过程中所经历的一系列阶段。在 Vue 中，每个组件都有自己的生命周期，可以通过生命周期钩子函数来监听和处理组件在不同阶段的行为和状态。",{"path":830,"title":831,"description":11},"\u002Ftutorials\u002Ffront-end\u002Fvue3\u002Fother-api","10. 其他 API",{"path":833,"title":834,"description":11},"\u002Ftutorials\u002Ffront-end\u002Fvue3\u002Fpinia","8. Pinia",{"path":836,"title":837,"description":11},"\u002Ftutorials\u002Ffront-end\u002Fvue3\u002Frouter","7. 路由",{"path":839,"title":840,"description":11},"\u002Ftutorials\u002Ffront-end\u002Fvue3\u002Ftemplate-grammar","3. 指令及模板语法",{"path":842,"title":843,"description":11},"\u002Ftutorials\u002Ffront-end\u002Fvue3\u002Fvue3-new-component","11. Vue3 新组件",{"path":845,"title":846,"description":847},"\u002Ftutorials\u002Ffront-end\u002Fvue3\u002Fwatch","5. 监视","Watch 是 Vue 提供的一个用于监视响应式数据变化并执行相应操作的 API，能够对响应式数据的变化做出一些操作的功能。Vue3 中的 Watch 支持多种用法，包括监视响应式对象、ref 对象、数组、函数等。",{"path":849,"title":850,"description":11},"\u002Ftutorials\u002Fmq\u002Fkafka\u002Fkafka-introduction","Kafka 简介与安装",{"path":852,"title":853,"description":11},"\u002Ftutorials\u002Fmq\u002Fkafka\u002Fkafka-producer-consumer","Kafka 生产者与消费者",{"path":855,"title":856,"description":857},"\u002Ftutorials\u002Fmq\u002Fkafka\u002Fkafka-springboot","Spring Boot 整合 Kafka","Spring Kafka 提供了对 Apache Kafka 的便捷集成。",{"path":859,"title":860,"description":861},"\u002Ftutorials\u002Fmq\u002Frabbitmq\u002Frabbitmq-exchange","RabbitMQ Exchange 详解","Exchange（交换机）是 RabbitMQ 的核心组件，负责接收生产者发送的消息，并根据规则将消息路由到一个或多个队列。",{"path":863,"title":864,"description":11},"\u002Ftutorials\u002Fmq\u002Frabbitmq\u002Frabbitmq-introduction","RabbitMQ 简介与安装",{"path":866,"title":867,"description":868},"\u002Ftutorials\u002Fmq\u002Frabbitmq\u002Frabbitmq-reliability","RabbitMQ 消息可靠性","消息可靠性是消息队列的核心要求，RabbitMQ 提供了多种机制来保证消息不丢失。",{"path":870,"title":871,"description":872},"\u002Ftutorials\u002Fmq\u002Frabbitmq\u002Frabbitmq-springboot","Spring Boot 整合 RabbitMQ","Spring AMQP 提供了对 RabbitMQ 的便捷集成，大大简化了开发工作。",{"path":874,"title":875,"description":11},"\u002Ftutorials\u002Fmq\u002Frocketmq\u002Frocketmq-client","RocketMQ 客户端使用",{"path":877,"title":878,"description":11},"\u002Ftutorials\u002Fmq\u002Frocketmq\u002Frocketmq-concepts","RocketMQ 核心概念",{"path":880,"title":881,"description":11},"\u002Ftutorials\u002Fmq\u002Frocketmq\u002Frocketmq-installation","RocketMQ 安装部署",{"path":883,"title":884,"description":885},"\u002Ftutorials\u002Fmq\u002Frocketmq\u002Frocketmq-message-type","RocketMQ 消息类型","RocketMQ 支持多种消息类型，满足不同业务场景需求。",{"id":887,"title":114,"body":888,"description":115,"extension":6002,"meta":6003,"navigation":1054,"path":113,"seo":6004,"stem":6005,"__hash__":6006},"docs\u002Fai\u002Frag\u002Fpractice.md",{"type":889,"value":890,"toc":5962},"minimark",[891,895,899,922,925,936,939,943,1012,1016,1067,1071,1075,1078,1082,1240,1244,1286,1289,1399,1402,1444,1448,1452,1455,1458,1464,1704,1708,1711,1963,1967,1970,2119,2122,2233,2236,2411,2415,2418,2421,2906,2910,2913,3177,3180,3398,3401,3404,4736,4739,4775,4778,4781,4785,4788,4966,4969,4973,4976,5140,5144,5147,5301,5305,5308,5491,5494,5497,5500,5561,5565,5568,5823,5826,5829,5856,5860,5863,5946,5949,5955,5958],[892,893,894],"h2",{"id":894},"实战目标",[896,897,898],"p",{},"本章将带你从零构建一个完整的 RAG 文档问答系统。我们将使用以下技术栈：",[900,901,902,910,916],"ul",{},[903,904,905,909],"li",{},[906,907,908],"strong",{},"LangChain","：RAG 应用开发框架",[903,911,912,915],{},[906,913,914],{},"ChromaDB","：向量数据库",[903,917,918,921],{},[906,919,920],{},"OpenAI","：嵌入模型 + 大语言模型",[896,923,924],{},"最终实现的效果：用户可以上传自己的文档（PDF、Markdown 等），然后通过自然语言提问获取基于文档内容的准确回答。",[926,927,933],"pre",{"className":928,"code":930,"language":931,"meta":932},[929],"language-text","flowchart TD\n    Doc[\"你的文档#40;PDF\u002FMarkdown\u002FTXT#41;\"]\n    DB[\"ChromaDB 向量数据库\"]\n    Chunks[\"检索到的相关片段 + 用户问题\"]\n    LLM[\"OpenAI GPT → 生成有依据的回答\"]\n    Doc -->|加载 & 分块 & 嵌入| DB\n    DB -->|用户提问 → 语义检索| Chunks\n    Chunks -->|组合 Prompt| LLM\n","text","mermaid",[934,935,930],"code",{"__ignoreMap":11},[892,937,938],{"id":938},"环境准备",[940,941,942],"h3",{"id":942},"安装依赖",[926,944,948],{"className":945,"code":946,"language":947,"meta":11,"style":11},"language-bash shiki shiki-themes github-light github-light github-dark","pip install langchain langchain-openai langchain-chroma langchain-community\npip install chromadb\npip install pypdf     # PDF 文档加载\npip install unstructured  # 多格式文档加载（可选）\n","bash",[934,949,950,975,985,999],{"__ignoreMap":11},[951,952,955,959,963,966,969,972],"span",{"class":953,"line":954},"line",1,[951,956,958],{"class":957},"snPdu","pip",[951,960,962],{"class":961},"sIIMD"," install",[951,964,965],{"class":961}," langchain",[951,967,968],{"class":961}," langchain-openai",[951,970,971],{"class":961}," langchain-chroma",[951,973,974],{"class":961}," langchain-community\n",[951,976,978,980,982],{"class":953,"line":977},2,[951,979,958],{"class":957},[951,981,962],{"class":961},[951,983,984],{"class":961}," chromadb\n",[951,986,988,990,992,995],{"class":953,"line":987},3,[951,989,958],{"class":957},[951,991,962],{"class":961},[951,993,994],{"class":961}," pypdf",[951,996,998],{"class":997},"sCsY4","     # PDF 文档加载\n",[951,1000,1002,1004,1006,1009],{"class":953,"line":1001},4,[951,1003,958],{"class":957},[951,1005,962],{"class":961},[951,1007,1008],{"class":961}," unstructured",[951,1010,1011],{"class":997},"  # 多格式文档加载（可选）\n",[940,1013,1015],{"id":1014},"配置-api-key","配置 API Key",[926,1017,1021],{"className":1018,"code":1019,"language":1020,"meta":11,"style":11},"language-python shiki shiki-themes github-light github-light github-dark","import os\nos.environ[\"OPENAI_API_KEY\"] = \"sk-your-api-key\"\n\n# 如果使用国内代理\n# os.environ[\"OPENAI_API_BASE\"] = \"https:\u002F\u002Fyour-proxy.com\u002Fv1\"\n","python",[934,1022,1023,1033,1050,1056,1061],{"__ignoreMap":11},[951,1024,1025,1029],{"class":953,"line":954},[951,1026,1028],{"class":1027},"s8jYJ","import",[951,1030,1032],{"class":1031},"sxrX7"," os\n",[951,1034,1035,1038,1041,1044,1047],{"class":953,"line":977},[951,1036,1037],{"class":1031},"os.environ[",[951,1039,1040],{"class":961},"\"OPENAI_API_KEY\"",[951,1042,1043],{"class":1031},"] ",[951,1045,1046],{"class":1027},"=",[951,1048,1049],{"class":961}," \"sk-your-api-key\"\n",[951,1051,1052],{"class":953,"line":987},[951,1053,1055],{"emptyLinePlaceholder":1054},true,"\n",[951,1057,1058],{"class":953,"line":1001},[951,1059,1060],{"class":997},"# 如果使用国内代理\n",[951,1062,1064],{"class":953,"line":1063},5,[951,1065,1066],{"class":997},"# os.environ[\"OPENAI_API_BASE\"] = \"https:\u002F\u002Fyour-proxy.com\u002Fv1\"\n",[1068,1069,1070],"warning",{},"\n请勿将 API Key 硬编码在代码中并提交到版本控制系统。建议使用环境变量或 `.env` 文件管理密钥，并将 `.env` 加入 `.gitignore`。\n",[892,1072,1074],{"id":1073},"第一步文档加载","第一步：文档加载",[896,1076,1077],{},"LangChain 提供了丰富的文档加载器（Document Loaders），支持多种文档格式。",[940,1079,1081],{"id":1080},"加载-pdf-文档","加载 PDF 文档",[926,1083,1085],{"className":1018,"code":1084,"language":1020,"meta":11,"style":11},"from langchain_community.document_loaders import PyPDFLoader\n\n# 加载单个 PDF 文件\nloader = PyPDFLoader(\".\u002Fdocs\u002Ftechnical_guide.pdf\")\ndocuments = loader.load()\n\nprint(f\"加载了 {len(documents)} 页文档\")\nprint(f\"第一页内容预览: {documents[0].page_content[:200]}\")\nprint(f\"元数据: {documents[0].metadata}\")\n# 输出: {'source': '.\u002Fdocs\u002Ftechnical_guide.pdf', 'page': 0}\n",[934,1086,1087,1100,1104,1109,1125,1135,1140,1170,1207,1234],{"__ignoreMap":11},[951,1088,1089,1092,1095,1097],{"class":953,"line":954},[951,1090,1091],{"class":1027},"from",[951,1093,1094],{"class":1031}," langchain_community.document_loaders ",[951,1096,1028],{"class":1027},[951,1098,1099],{"class":1031}," PyPDFLoader\n",[951,1101,1102],{"class":953,"line":977},[951,1103,1055],{"emptyLinePlaceholder":1054},[951,1105,1106],{"class":953,"line":987},[951,1107,1108],{"class":997},"# 加载单个 PDF 文件\n",[951,1110,1111,1114,1116,1119,1122],{"class":953,"line":1001},[951,1112,1113],{"class":1031},"loader ",[951,1115,1046],{"class":1027},[951,1117,1118],{"class":1031}," PyPDFLoader(",[951,1120,1121],{"class":961},"\".\u002Fdocs\u002Ftechnical_guide.pdf\"",[951,1123,1124],{"class":1031},")\n",[951,1126,1127,1130,1132],{"class":953,"line":1063},[951,1128,1129],{"class":1031},"documents ",[951,1131,1046],{"class":1027},[951,1133,1134],{"class":1031}," loader.load()\n",[951,1136,1138],{"class":953,"line":1137},6,[951,1139,1055],{"emptyLinePlaceholder":1054},[951,1141,1143,1147,1150,1153,1156,1159,1162,1165,1168],{"class":953,"line":1142},7,[951,1144,1146],{"class":1145},"sBjJW","print",[951,1148,1149],{"class":1031},"(",[951,1151,1152],{"class":1027},"f",[951,1154,1155],{"class":961},"\"加载了 ",[951,1157,1158],{"class":1145},"{len",[951,1160,1161],{"class":1031},"(documents)",[951,1163,1164],{"class":1145},"}",[951,1166,1167],{"class":961}," 页文档\"",[951,1169,1124],{"class":1031},[951,1171,1173,1175,1177,1179,1182,1185,1188,1191,1194,1197,1200,1202,1205],{"class":953,"line":1172},8,[951,1174,1146],{"class":1145},[951,1176,1149],{"class":1031},[951,1178,1152],{"class":1027},[951,1180,1181],{"class":961},"\"第一页内容预览: ",[951,1183,1184],{"class":1145},"{",[951,1186,1187],{"class":1031},"documents[",[951,1189,1190],{"class":1145},"0",[951,1192,1193],{"class":1031},"].page_content[:",[951,1195,1196],{"class":1145},"200",[951,1198,1199],{"class":1031},"]",[951,1201,1164],{"class":1145},[951,1203,1204],{"class":961},"\"",[951,1206,1124],{"class":1031},[951,1208,1210,1212,1214,1216,1219,1221,1223,1225,1228,1230,1232],{"class":953,"line":1209},9,[951,1211,1146],{"class":1145},[951,1213,1149],{"class":1031},[951,1215,1152],{"class":1027},[951,1217,1218],{"class":961},"\"元数据: ",[951,1220,1184],{"class":1145},[951,1222,1187],{"class":1031},[951,1224,1190],{"class":1145},[951,1226,1227],{"class":1031},"].metadata",[951,1229,1164],{"class":1145},[951,1231,1204],{"class":961},[951,1233,1124],{"class":1031},[951,1235,1237],{"class":953,"line":1236},10,[951,1238,1239],{"class":997},"# 输出: {'source': '.\u002Fdocs\u002Ftechnical_guide.pdf', 'page': 0}\n",[940,1241,1243],{"id":1242},"加载-markdown-文档","加载 Markdown 文档",[926,1245,1247],{"className":1018,"code":1246,"language":1020,"meta":11,"style":11},"from langchain_community.document_loaders import UnstructuredMarkdownLoader\n\nloader = UnstructuredMarkdownLoader(\".\u002Fdocs\u002Freadme.md\")\ndocuments = loader.load()\n",[934,1248,1249,1260,1264,1278],{"__ignoreMap":11},[951,1250,1251,1253,1255,1257],{"class":953,"line":954},[951,1252,1091],{"class":1027},[951,1254,1094],{"class":1031},[951,1256,1028],{"class":1027},[951,1258,1259],{"class":1031}," UnstructuredMarkdownLoader\n",[951,1261,1262],{"class":953,"line":977},[951,1263,1055],{"emptyLinePlaceholder":1054},[951,1265,1266,1268,1270,1273,1276],{"class":953,"line":987},[951,1267,1113],{"class":1031},[951,1269,1046],{"class":1027},[951,1271,1272],{"class":1031}," UnstructuredMarkdownLoader(",[951,1274,1275],{"class":961},"\".\u002Fdocs\u002Freadme.md\"",[951,1277,1124],{"class":1031},[951,1279,1280,1282,1284],{"class":953,"line":1001},[951,1281,1129],{"class":1031},[951,1283,1046],{"class":1027},[951,1285,1134],{"class":1031},[940,1287,1288],{"id":1288},"批量加载目录下的文件",[926,1290,1292],{"className":1018,"code":1291,"language":1020,"meta":11,"style":11},"from langchain_community.document_loaders import DirectoryLoader, PyPDFLoader\n\n# 加载目录下所有 PDF 文件\nloader = DirectoryLoader(\n    \".\u002Fdocs\u002F\",\n    glob=\"**\u002F*.pdf\",\n    loader_cls=PyPDFLoader,\n    show_progress=True\n)\ndocuments = loader.load()\nprint(f\"共加载 {len(documents)} 个文档片段\")\n",[934,1293,1294,1305,1309,1314,1323,1331,1344,1354,1364,1368,1376],{"__ignoreMap":11},[951,1295,1296,1298,1300,1302],{"class":953,"line":954},[951,1297,1091],{"class":1027},[951,1299,1094],{"class":1031},[951,1301,1028],{"class":1027},[951,1303,1304],{"class":1031}," DirectoryLoader, PyPDFLoader\n",[951,1306,1307],{"class":953,"line":977},[951,1308,1055],{"emptyLinePlaceholder":1054},[951,1310,1311],{"class":953,"line":987},[951,1312,1313],{"class":997},"# 加载目录下所有 PDF 文件\n",[951,1315,1316,1318,1320],{"class":953,"line":1001},[951,1317,1113],{"class":1031},[951,1319,1046],{"class":1027},[951,1321,1322],{"class":1031}," DirectoryLoader(\n",[951,1324,1325,1328],{"class":953,"line":1063},[951,1326,1327],{"class":961},"    \".\u002Fdocs\u002F\"",[951,1329,1330],{"class":1031},",\n",[951,1332,1333,1337,1339,1342],{"class":953,"line":1137},[951,1334,1336],{"class":1335},"sP4rz","    glob",[951,1338,1046],{"class":1027},[951,1340,1341],{"class":961},"\"**\u002F*.pdf\"",[951,1343,1330],{"class":1031},[951,1345,1346,1349,1351],{"class":953,"line":1142},[951,1347,1348],{"class":1335},"    loader_cls",[951,1350,1046],{"class":1027},[951,1352,1353],{"class":1031},"PyPDFLoader,\n",[951,1355,1356,1359,1361],{"class":953,"line":1172},[951,1357,1358],{"class":1335},"    show_progress",[951,1360,1046],{"class":1027},[951,1362,1363],{"class":1145},"True\n",[951,1365,1366],{"class":953,"line":1209},[951,1367,1124],{"class":1031},[951,1369,1370,1372,1374],{"class":953,"line":1236},[951,1371,1129],{"class":1031},[951,1373,1046],{"class":1027},[951,1375,1134],{"class":1031},[951,1377,1379,1381,1383,1385,1388,1390,1392,1394,1397],{"class":953,"line":1378},11,[951,1380,1146],{"class":1145},[951,1382,1149],{"class":1031},[951,1384,1152],{"class":1027},[951,1386,1387],{"class":961},"\"共加载 ",[951,1389,1158],{"class":1145},[951,1391,1161],{"class":1031},[951,1393,1164],{"class":1145},[951,1395,1396],{"class":961}," 个文档片段\"",[951,1398,1124],{"class":1031},[940,1400,1401],{"id":1401},"加载网页内容",[926,1403,1405],{"className":1018,"code":1404,"language":1020,"meta":11,"style":11},"from langchain_community.document_loaders import WebBaseLoader\n\nloader = WebBaseLoader(\"https:\u002F\u002Fdocs.example.com\u002Fguide\")\ndocuments = loader.load()\n",[934,1406,1407,1418,1422,1436],{"__ignoreMap":11},[951,1408,1409,1411,1413,1415],{"class":953,"line":954},[951,1410,1091],{"class":1027},[951,1412,1094],{"class":1031},[951,1414,1028],{"class":1027},[951,1416,1417],{"class":1031}," WebBaseLoader\n",[951,1419,1420],{"class":953,"line":977},[951,1421,1055],{"emptyLinePlaceholder":1054},[951,1423,1424,1426,1428,1431,1434],{"class":953,"line":987},[951,1425,1113],{"class":1031},[951,1427,1046],{"class":1027},[951,1429,1430],{"class":1031}," WebBaseLoader(",[951,1432,1433],{"class":961},"\"https:\u002F\u002Fdocs.example.com\u002Fguide\"",[951,1435,1124],{"class":1031},[951,1437,1438,1440,1442],{"class":953,"line":1001},[951,1439,1129],{"class":1031},[951,1441,1046],{"class":1027},[951,1443,1134],{"class":1031},[1445,1446,1447],"tip",{},"\n在实际项目中，你可能需要加载多种格式的文档。建议封装一个统一的文档加载函数，根据文件扩展名自动选择对应的加载器。\n",[892,1449,1451],{"id":1450},"第二步文本分割","第二步：文本分割",[896,1453,1454],{},"将加载的文档切分为适合检索的小块。",[940,1456,1457],{"id":1457},"使用递归字符分割器",[896,1459,1460,1463],{},[934,1461,1462],{},"RecursiveCharacterTextSplitter"," 是 LangChain 中最推荐的通用文本分割器，它会按照一系列分隔符（如段落、句子、词）递归地分割文本，尽量保持语义完整性。",[926,1465,1467],{"className":1018,"code":1466,"language":1020,"meta":11,"style":11},"from langchain.text_splitter import RecursiveCharacterTextSplitter\n\ntext_splitter = RecursiveCharacterTextSplitter(\n    chunk_size=500,       # 每个分块的最大字符数\n    chunk_overlap=100,    # 相邻分块的重叠字符数\n    length_function=len,\n    separators=[\"\\n\\n\", \"\\n\", \"。\", \"！\", \"？\", \".\", \" \", \"\"]  # 分隔符优先级\n)\n\nchunks = text_splitter.split_documents(documents)\nprint(f\"文档被分成了 {len(chunks)} 个片段\")\nprint(f\"第一个片段长度: {len(chunks[0].page_content)} 字符\")\nprint(f\"第一个片段内容: {chunks[0].page_content[:100]}...\")\n",[934,1468,1469,1481,1485,1495,1511,1527,1539,1602,1606,1610,1620,1643,1672],{"__ignoreMap":11},[951,1470,1471,1473,1476,1478],{"class":953,"line":954},[951,1472,1091],{"class":1027},[951,1474,1475],{"class":1031}," langchain.text_splitter ",[951,1477,1028],{"class":1027},[951,1479,1480],{"class":1031}," RecursiveCharacterTextSplitter\n",[951,1482,1483],{"class":953,"line":977},[951,1484,1055],{"emptyLinePlaceholder":1054},[951,1486,1487,1490,1492],{"class":953,"line":987},[951,1488,1489],{"class":1031},"text_splitter ",[951,1491,1046],{"class":1027},[951,1493,1494],{"class":1031}," RecursiveCharacterTextSplitter(\n",[951,1496,1497,1500,1502,1505,1508],{"class":953,"line":1001},[951,1498,1499],{"class":1335},"    chunk_size",[951,1501,1046],{"class":1027},[951,1503,1504],{"class":1145},"500",[951,1506,1507],{"class":1031},",       ",[951,1509,1510],{"class":997},"# 每个分块的最大字符数\n",[951,1512,1513,1516,1518,1521,1524],{"class":953,"line":1063},[951,1514,1515],{"class":1335},"    chunk_overlap",[951,1517,1046],{"class":1027},[951,1519,1520],{"class":1145},"100",[951,1522,1523],{"class":1031},",    ",[951,1525,1526],{"class":997},"# 相邻分块的重叠字符数\n",[951,1528,1529,1532,1534,1537],{"class":953,"line":1137},[951,1530,1531],{"class":1335},"    length_function",[951,1533,1046],{"class":1027},[951,1535,1536],{"class":1145},"len",[951,1538,1330],{"class":1031},[951,1540,1541,1544,1546,1549,1551,1554,1556,1559,1561,1564,1566,1568,1571,1573,1576,1578,1581,1583,1586,1588,1591,1593,1596,1599],{"class":953,"line":1142},[951,1542,1543],{"class":1335},"    separators",[951,1545,1046],{"class":1027},[951,1547,1548],{"class":1031},"[",[951,1550,1204],{"class":961},[951,1552,1553],{"class":1145},"\\n\\n",[951,1555,1204],{"class":961},[951,1557,1558],{"class":1031},", ",[951,1560,1204],{"class":961},[951,1562,1563],{"class":1145},"\\n",[951,1565,1204],{"class":961},[951,1567,1558],{"class":1031},[951,1569,1570],{"class":961},"\"。\"",[951,1572,1558],{"class":1031},[951,1574,1575],{"class":961},"\"！\"",[951,1577,1558],{"class":1031},[951,1579,1580],{"class":961},"\"？\"",[951,1582,1558],{"class":1031},[951,1584,1585],{"class":961},"\".\"",[951,1587,1558],{"class":1031},[951,1589,1590],{"class":961},"\" \"",[951,1592,1558],{"class":1031},[951,1594,1595],{"class":961},"\"\"",[951,1597,1598],{"class":1031},"]  ",[951,1600,1601],{"class":997},"# 分隔符优先级\n",[951,1603,1604],{"class":953,"line":1172},[951,1605,1124],{"class":1031},[951,1607,1608],{"class":953,"line":1209},[951,1609,1055],{"emptyLinePlaceholder":1054},[951,1611,1612,1615,1617],{"class":953,"line":1236},[951,1613,1614],{"class":1031},"chunks ",[951,1616,1046],{"class":1027},[951,1618,1619],{"class":1031}," text_splitter.split_documents(documents)\n",[951,1621,1622,1624,1626,1628,1631,1633,1636,1638,1641],{"class":953,"line":1378},[951,1623,1146],{"class":1145},[951,1625,1149],{"class":1031},[951,1627,1152],{"class":1027},[951,1629,1630],{"class":961},"\"文档被分成了 ",[951,1632,1158],{"class":1145},[951,1634,1635],{"class":1031},"(chunks)",[951,1637,1164],{"class":1145},[951,1639,1640],{"class":961}," 个片段\"",[951,1642,1124],{"class":1031},[951,1644,1646,1648,1650,1652,1655,1657,1660,1662,1665,1667,1670],{"class":953,"line":1645},12,[951,1647,1146],{"class":1145},[951,1649,1149],{"class":1031},[951,1651,1152],{"class":1027},[951,1653,1654],{"class":961},"\"第一个片段长度: ",[951,1656,1158],{"class":1145},[951,1658,1659],{"class":1031},"(chunks[",[951,1661,1190],{"class":1145},[951,1663,1664],{"class":1031},"].page_content)",[951,1666,1164],{"class":1145},[951,1668,1669],{"class":961}," 字符\"",[951,1671,1124],{"class":1031},[951,1673,1675,1677,1679,1681,1684,1686,1689,1691,1693,1695,1697,1699,1702],{"class":953,"line":1674},13,[951,1676,1146],{"class":1145},[951,1678,1149],{"class":1031},[951,1680,1152],{"class":1027},[951,1682,1683],{"class":961},"\"第一个片段内容: ",[951,1685,1184],{"class":1145},[951,1687,1688],{"class":1031},"chunks[",[951,1690,1190],{"class":1145},[951,1692,1193],{"class":1031},[951,1694,1520],{"class":1145},[951,1696,1199],{"class":1031},[951,1698,1164],{"class":1145},[951,1700,1701],{"class":961},"...\"",[951,1703,1124],{"class":1031},[940,1705,1707],{"id":1706},"使用-markdown-专用分割器","使用 Markdown 专用分割器",[896,1709,1710],{},"如果你的文档是 Markdown 格式，可以使用按标题层级分割的分割器：",[926,1712,1714],{"className":1018,"code":1713,"language":1020,"meta":11,"style":11},"from langchain.text_splitter import MarkdownHeaderTextSplitter\n\nheaders_to_split_on = [\n    (\"#\", \"Header 1\"),\n    (\"##\", \"Header 2\"),\n    (\"###\", \"Header 3\"),\n]\n\nmd_splitter = MarkdownHeaderTextSplitter(headers_to_split_on=headers_to_split_on)\n\nmd_text = \"\"\"\n# Kubernetes 入门\n\n## 什么是 Kubernetes\nKubernetes 是一个开源的容器编排平台...\n\n## 核心概念\n### Pod\nPod 是 Kubernetes 中最小的可部署单元...\n\n### Service\nService 定义了一组 Pod 的访问策略...\n\"\"\"\n\nmd_chunks = md_splitter.split_text(md_text)\nfor chunk in md_chunks:\n    print(f\"标题: {chunk.metadata} | 内容: {chunk.page_content[:50]}...\")\n",[934,1715,1716,1727,1731,1741,1757,1771,1785,1790,1794,1812,1816,1826,1831,1835,1841,1847,1852,1858,1864,1870,1875,1881,1887,1893,1898,1909,1924],{"__ignoreMap":11},[951,1717,1718,1720,1722,1724],{"class":953,"line":954},[951,1719,1091],{"class":1027},[951,1721,1475],{"class":1031},[951,1723,1028],{"class":1027},[951,1725,1726],{"class":1031}," MarkdownHeaderTextSplitter\n",[951,1728,1729],{"class":953,"line":977},[951,1730,1055],{"emptyLinePlaceholder":1054},[951,1732,1733,1736,1738],{"class":953,"line":987},[951,1734,1735],{"class":1031},"headers_to_split_on ",[951,1737,1046],{"class":1027},[951,1739,1740],{"class":1031}," [\n",[951,1742,1743,1746,1749,1751,1754],{"class":953,"line":1001},[951,1744,1745],{"class":1031},"    (",[951,1747,1748],{"class":961},"\"#\"",[951,1750,1558],{"class":1031},[951,1752,1753],{"class":961},"\"Header 1\"",[951,1755,1756],{"class":1031},"),\n",[951,1758,1759,1761,1764,1766,1769],{"class":953,"line":1063},[951,1760,1745],{"class":1031},[951,1762,1763],{"class":961},"\"##\"",[951,1765,1558],{"class":1031},[951,1767,1768],{"class":961},"\"Header 2\"",[951,1770,1756],{"class":1031},[951,1772,1773,1775,1778,1780,1783],{"class":953,"line":1137},[951,1774,1745],{"class":1031},[951,1776,1777],{"class":961},"\"###\"",[951,1779,1558],{"class":1031},[951,1781,1782],{"class":961},"\"Header 3\"",[951,1784,1756],{"class":1031},[951,1786,1787],{"class":953,"line":1142},[951,1788,1789],{"class":1031},"]\n",[951,1791,1792],{"class":953,"line":1172},[951,1793,1055],{"emptyLinePlaceholder":1054},[951,1795,1796,1799,1801,1804,1807,1809],{"class":953,"line":1209},[951,1797,1798],{"class":1031},"md_splitter ",[951,1800,1046],{"class":1027},[951,1802,1803],{"class":1031}," MarkdownHeaderTextSplitter(",[951,1805,1806],{"class":1335},"headers_to_split_on",[951,1808,1046],{"class":1027},[951,1810,1811],{"class":1031},"headers_to_split_on)\n",[951,1813,1814],{"class":953,"line":1236},[951,1815,1055],{"emptyLinePlaceholder":1054},[951,1817,1818,1821,1823],{"class":953,"line":1378},[951,1819,1820],{"class":1031},"md_text ",[951,1822,1046],{"class":1027},[951,1824,1825],{"class":961}," \"\"\"\n",[951,1827,1828],{"class":953,"line":1645},[951,1829,1830],{"class":961},"# Kubernetes 入门\n",[951,1832,1833],{"class":953,"line":1674},[951,1834,1055],{"emptyLinePlaceholder":1054},[951,1836,1838],{"class":953,"line":1837},14,[951,1839,1840],{"class":961},"## 什么是 Kubernetes\n",[951,1842,1844],{"class":953,"line":1843},15,[951,1845,1846],{"class":961},"Kubernetes 是一个开源的容器编排平台...\n",[951,1848,1850],{"class":953,"line":1849},16,[951,1851,1055],{"emptyLinePlaceholder":1054},[951,1853,1855],{"class":953,"line":1854},17,[951,1856,1857],{"class":961},"## 核心概念\n",[951,1859,1861],{"class":953,"line":1860},18,[951,1862,1863],{"class":961},"### Pod\n",[951,1865,1867],{"class":953,"line":1866},19,[951,1868,1869],{"class":961},"Pod 是 Kubernetes 中最小的可部署单元...\n",[951,1871,1873],{"class":953,"line":1872},20,[951,1874,1055],{"emptyLinePlaceholder":1054},[951,1876,1878],{"class":953,"line":1877},21,[951,1879,1880],{"class":961},"### Service\n",[951,1882,1884],{"class":953,"line":1883},22,[951,1885,1886],{"class":961},"Service 定义了一组 Pod 的访问策略...\n",[951,1888,1890],{"class":953,"line":1889},23,[951,1891,1892],{"class":961},"\"\"\"\n",[951,1894,1896],{"class":953,"line":1895},24,[951,1897,1055],{"emptyLinePlaceholder":1054},[951,1899,1901,1904,1906],{"class":953,"line":1900},25,[951,1902,1903],{"class":1031},"md_chunks ",[951,1905,1046],{"class":1027},[951,1907,1908],{"class":1031}," md_splitter.split_text(md_text)\n",[951,1910,1912,1915,1918,1921],{"class":953,"line":1911},26,[951,1913,1914],{"class":1027},"for",[951,1916,1917],{"class":1031}," chunk ",[951,1919,1920],{"class":1027},"in",[951,1922,1923],{"class":1031}," md_chunks:\n",[951,1925,1927,1930,1932,1934,1937,1939,1942,1944,1947,1949,1952,1955,1957,1959,1961],{"class":953,"line":1926},27,[951,1928,1929],{"class":1145},"    print",[951,1931,1149],{"class":1031},[951,1933,1152],{"class":1027},[951,1935,1936],{"class":961},"\"标题: ",[951,1938,1184],{"class":1145},[951,1940,1941],{"class":1031},"chunk.metadata",[951,1943,1164],{"class":1145},[951,1945,1946],{"class":961}," | 内容: ",[951,1948,1184],{"class":1145},[951,1950,1951],{"class":1031},"chunk.page_content[:",[951,1953,1954],{"class":1145},"50",[951,1956,1199],{"class":1031},[951,1958,1164],{"class":1145},[951,1960,1701],{"class":961},[951,1962,1124],{"class":1031},[892,1964,1966],{"id":1965},"第三步嵌入与向量存储","第三步：嵌入与向量存储",[896,1968,1969],{},"将文本片段转换为向量并存入 ChromaDB。",[926,1971,1973],{"className":1018,"code":1972,"language":1020,"meta":11,"style":11},"from langchain_openai import OpenAIEmbeddings\nfrom langchain_chroma import Chroma\n\n# 初始化嵌入模型\nembeddings = OpenAIEmbeddings(\n    model=\"text-embedding-3-small\",\n    # dimensions=512  # 可选：降维以减少存储空间\n)\n\n# 创建向量存储（内存模式，适合测试）\nvectorstore = Chroma.from_documents(\n    documents=chunks,\n    embedding=embeddings,\n    collection_name=\"my_knowledge_base\"\n)\n\nprint(f\"已存储 {vectorstore._collection.count()} 个向量\")\n",[934,1974,1975,1987,1999,2003,2008,2018,2030,2035,2039,2043,2048,2058,2068,2078,2088,2092,2096],{"__ignoreMap":11},[951,1976,1977,1979,1982,1984],{"class":953,"line":954},[951,1978,1091],{"class":1027},[951,1980,1981],{"class":1031}," langchain_openai ",[951,1983,1028],{"class":1027},[951,1985,1986],{"class":1031}," OpenAIEmbeddings\n",[951,1988,1989,1991,1994,1996],{"class":953,"line":977},[951,1990,1091],{"class":1027},[951,1992,1993],{"class":1031}," langchain_chroma ",[951,1995,1028],{"class":1027},[951,1997,1998],{"class":1031}," Chroma\n",[951,2000,2001],{"class":953,"line":987},[951,2002,1055],{"emptyLinePlaceholder":1054},[951,2004,2005],{"class":953,"line":1001},[951,2006,2007],{"class":997},"# 初始化嵌入模型\n",[951,2009,2010,2013,2015],{"class":953,"line":1063},[951,2011,2012],{"class":1031},"embeddings ",[951,2014,1046],{"class":1027},[951,2016,2017],{"class":1031}," OpenAIEmbeddings(\n",[951,2019,2020,2023,2025,2028],{"class":953,"line":1137},[951,2021,2022],{"class":1335},"    model",[951,2024,1046],{"class":1027},[951,2026,2027],{"class":961},"\"text-embedding-3-small\"",[951,2029,1330],{"class":1031},[951,2031,2032],{"class":953,"line":1142},[951,2033,2034],{"class":997},"    # dimensions=512  # 可选：降维以减少存储空间\n",[951,2036,2037],{"class":953,"line":1172},[951,2038,1124],{"class":1031},[951,2040,2041],{"class":953,"line":1209},[951,2042,1055],{"emptyLinePlaceholder":1054},[951,2044,2045],{"class":953,"line":1236},[951,2046,2047],{"class":997},"# 创建向量存储（内存模式，适合测试）\n",[951,2049,2050,2053,2055],{"class":953,"line":1378},[951,2051,2052],{"class":1031},"vectorstore ",[951,2054,1046],{"class":1027},[951,2056,2057],{"class":1031}," Chroma.from_documents(\n",[951,2059,2060,2063,2065],{"class":953,"line":1645},[951,2061,2062],{"class":1335},"    documents",[951,2064,1046],{"class":1027},[951,2066,2067],{"class":1031},"chunks,\n",[951,2069,2070,2073,2075],{"class":953,"line":1674},[951,2071,2072],{"class":1335},"    embedding",[951,2074,1046],{"class":1027},[951,2076,2077],{"class":1031},"embeddings,\n",[951,2079,2080,2083,2085],{"class":953,"line":1837},[951,2081,2082],{"class":1335},"    collection_name",[951,2084,1046],{"class":1027},[951,2086,2087],{"class":961},"\"my_knowledge_base\"\n",[951,2089,2090],{"class":953,"line":1843},[951,2091,1124],{"class":1031},[951,2093,2094],{"class":953,"line":1849},[951,2095,1055],{"emptyLinePlaceholder":1054},[951,2097,2098,2100,2102,2104,2107,2109,2112,2114,2117],{"class":953,"line":1854},[951,2099,1146],{"class":1145},[951,2101,1149],{"class":1031},[951,2103,1152],{"class":1027},[951,2105,2106],{"class":961},"\"已存储 ",[951,2108,1184],{"class":1145},[951,2110,2111],{"class":1031},"vectorstore._collection.count()",[951,2113,1164],{"class":1145},[951,2115,2116],{"class":961}," 个向量\"",[951,2118,1124],{"class":1031},[940,2120,2121],{"id":2121},"持久化存储",[926,2123,2125],{"className":1018,"code":2124,"language":1020,"meta":11,"style":11},"# 持久化到磁盘（适合生产使用）\nvectorstore = Chroma.from_documents(\n    documents=chunks,\n    embedding=embeddings,\n    persist_directory=\".\u002Fchroma_db\",  # 存储路径\n    collection_name=\"my_knowledge_base\"\n)\n\n# 后续使用时，直接从磁盘加载\nvectorstore = Chroma(\n    persist_directory=\".\u002Fchroma_db\",\n    embedding_function=embeddings,\n    collection_name=\"my_knowledge_base\"\n)\n",[934,2126,2127,2132,2140,2148,2156,2172,2180,2184,2188,2193,2202,2212,2221,2229],{"__ignoreMap":11},[951,2128,2129],{"class":953,"line":954},[951,2130,2131],{"class":997},"# 持久化到磁盘（适合生产使用）\n",[951,2133,2134,2136,2138],{"class":953,"line":977},[951,2135,2052],{"class":1031},[951,2137,1046],{"class":1027},[951,2139,2057],{"class":1031},[951,2141,2142,2144,2146],{"class":953,"line":987},[951,2143,2062],{"class":1335},[951,2145,1046],{"class":1027},[951,2147,2067],{"class":1031},[951,2149,2150,2152,2154],{"class":953,"line":1001},[951,2151,2072],{"class":1335},[951,2153,1046],{"class":1027},[951,2155,2077],{"class":1031},[951,2157,2158,2161,2163,2166,2169],{"class":953,"line":1063},[951,2159,2160],{"class":1335},"    persist_directory",[951,2162,1046],{"class":1027},[951,2164,2165],{"class":961},"\".\u002Fchroma_db\"",[951,2167,2168],{"class":1031},",  ",[951,2170,2171],{"class":997},"# 存储路径\n",[951,2173,2174,2176,2178],{"class":953,"line":1137},[951,2175,2082],{"class":1335},[951,2177,1046],{"class":1027},[951,2179,2087],{"class":961},[951,2181,2182],{"class":953,"line":1142},[951,2183,1124],{"class":1031},[951,2185,2186],{"class":953,"line":1172},[951,2187,1055],{"emptyLinePlaceholder":1054},[951,2189,2190],{"class":953,"line":1209},[951,2191,2192],{"class":997},"# 后续使用时，直接从磁盘加载\n",[951,2194,2195,2197,2199],{"class":953,"line":1236},[951,2196,2052],{"class":1031},[951,2198,1046],{"class":1027},[951,2200,2201],{"class":1031}," Chroma(\n",[951,2203,2204,2206,2208,2210],{"class":953,"line":1378},[951,2205,2160],{"class":1335},[951,2207,1046],{"class":1027},[951,2209,2165],{"class":961},[951,2211,1330],{"class":1031},[951,2213,2214,2217,2219],{"class":953,"line":1645},[951,2215,2216],{"class":1335},"    embedding_function",[951,2218,1046],{"class":1027},[951,2220,2077],{"class":1031},[951,2222,2223,2225,2227],{"class":953,"line":1674},[951,2224,2082],{"class":1335},[951,2226,1046],{"class":1027},[951,2228,2087],{"class":961},[951,2230,2231],{"class":953,"line":1837},[951,2232,1124],{"class":1031},[940,2234,2235],{"id":2235},"测试检索",[926,2237,2239],{"className":1018,"code":2238,"language":1020,"meta":11,"style":11},"# 测试相似度搜索\nquery = \"Kubernetes 的核心概念有哪些？\"\nresults = vectorstore.similarity_search(query, k=3)\n\nprint(f\"查询: {query}\\n\")\nfor i, doc in enumerate(results):\n    print(f\"--- 结果 {i+1} ---\")\n    print(f\"来源: {doc.metadata.get('source', 'unknown')}\")\n    print(f\"内容: {doc.page_content[:200]}\")\n    print()\n",[934,2240,2241,2246,2256,2276,2280,2303,2318,2345,2378,2404],{"__ignoreMap":11},[951,2242,2243],{"class":953,"line":954},[951,2244,2245],{"class":997},"# 测试相似度搜索\n",[951,2247,2248,2251,2253],{"class":953,"line":977},[951,2249,2250],{"class":1031},"query ",[951,2252,1046],{"class":1027},[951,2254,2255],{"class":961}," \"Kubernetes 的核心概念有哪些？\"\n",[951,2257,2258,2261,2263,2266,2269,2271,2274],{"class":953,"line":987},[951,2259,2260],{"class":1031},"results ",[951,2262,1046],{"class":1027},[951,2264,2265],{"class":1031}," vectorstore.similarity_search(query, ",[951,2267,2268],{"class":1335},"k",[951,2270,1046],{"class":1027},[951,2272,2273],{"class":1145},"3",[951,2275,1124],{"class":1031},[951,2277,2278],{"class":953,"line":1001},[951,2279,1055],{"emptyLinePlaceholder":1054},[951,2281,2282,2284,2286,2288,2291,2293,2296,2299,2301],{"class":953,"line":1063},[951,2283,1146],{"class":1145},[951,2285,1149],{"class":1031},[951,2287,1152],{"class":1027},[951,2289,2290],{"class":961},"\"查询: ",[951,2292,1184],{"class":1145},[951,2294,2295],{"class":1031},"query",[951,2297,2298],{"class":1145},"}\\n",[951,2300,1204],{"class":961},[951,2302,1124],{"class":1031},[951,2304,2305,2307,2310,2312,2315],{"class":953,"line":1137},[951,2306,1914],{"class":1027},[951,2308,2309],{"class":1031}," i, doc ",[951,2311,1920],{"class":1027},[951,2313,2314],{"class":1145}," enumerate",[951,2316,2317],{"class":1031},"(results):\n",[951,2319,2320,2322,2324,2326,2329,2331,2334,2337,2340,2343],{"class":953,"line":1142},[951,2321,1929],{"class":1145},[951,2323,1149],{"class":1031},[951,2325,1152],{"class":1027},[951,2327,2328],{"class":961},"\"--- 结果 ",[951,2330,1184],{"class":1145},[951,2332,2333],{"class":1031},"i",[951,2335,2336],{"class":1027},"+",[951,2338,2339],{"class":1145},"1}",[951,2341,2342],{"class":961}," ---\"",[951,2344,1124],{"class":1031},[951,2346,2347,2349,2351,2353,2356,2358,2361,2364,2366,2369,2372,2374,2376],{"class":953,"line":1172},[951,2348,1929],{"class":1145},[951,2350,1149],{"class":1031},[951,2352,1152],{"class":1027},[951,2354,2355],{"class":961},"\"来源: ",[951,2357,1184],{"class":1145},[951,2359,2360],{"class":1031},"doc.metadata.get(",[951,2362,2363],{"class":961},"'source'",[951,2365,1558],{"class":1031},[951,2367,2368],{"class":961},"'unknown'",[951,2370,2371],{"class":1031},")",[951,2373,1164],{"class":1145},[951,2375,1204],{"class":961},[951,2377,1124],{"class":1031},[951,2379,2380,2382,2384,2386,2389,2391,2394,2396,2398,2400,2402],{"class":953,"line":1209},[951,2381,1929],{"class":1145},[951,2383,1149],{"class":1031},[951,2385,1152],{"class":1027},[951,2387,2388],{"class":961},"\"内容: ",[951,2390,1184],{"class":1145},[951,2392,2393],{"class":1031},"doc.page_content[:",[951,2395,1196],{"class":1145},[951,2397,1199],{"class":1031},[951,2399,1164],{"class":1145},[951,2401,1204],{"class":961},[951,2403,1124],{"class":1031},[951,2405,2406,2408],{"class":953,"line":1236},[951,2407,1929],{"class":1145},[951,2409,2410],{"class":1031},"()\n",[892,2412,2414],{"id":2413},"第四步构建检索链","第四步：构建检索链",[896,2416,2417],{},"将检索器与大语言模型组合，构建完整的 RAG 问答链。",[940,2419,2420],{"id":2420},"基础问答链",[926,2422,2424],{"className":1018,"code":2423,"language":1020,"meta":11,"style":11},"from langchain_openai import ChatOpenAI\nfrom langchain.chains import RetrievalQA\nfrom langchain.prompts import PromptTemplate\n\n# 初始化 LLM\nllm = ChatOpenAI(\n    model=\"gpt-4o-mini\",\n    temperature=0  # 降低随机性，使回答更稳定\n)\n\n# 自定义 Prompt 模板\nprompt_template = PromptTemplate(\n    input_variables=[\"context\", \"question\"],\n    template=\"\"\"你是一个专业的技术文档助手。请基于以下参考信息回答用户的问题。\n\n要求：\n1. 只基于提供的参考信息回答，不要编造信息\n2. 如果参考信息中没有相关内容，请明确告知用户\n3. 回答要准确、条理清晰\n4. 适当引用原文内容\n\n参考信息：\n{context}\n\n用户问题：{question}\n\n请回答：\"\"\"\n)\n\n# 创建检索器\nretriever = vectorstore.as_retriever(\n    search_type=\"similarity\",   # 相似度搜索\n    search_kwargs={\"k\": 4}      # 返回 top-4 个结果\n)\n\n# 创建 RAG 链\nqa_chain = RetrievalQA.from_chain_type(\n    llm=llm,\n    chain_type=\"stuff\",  # 将所有检索结果拼接到 prompt 中\n    retriever=retriever,\n    chain_type_kwargs={\"prompt\": prompt_template},\n    return_source_documents=True  # 返回源文档\n)\n\n# 提问\nresponse = qa_chain.invoke({\"query\": \"Kubernetes 中 Pod 和 Service 的区别是什么？\"})\nprint(\"回答:\", response[\"result\"])\nprint(\"\\n参考文档:\")\nfor doc in response[\"source_documents\"]:\n    print(f\"  - {doc.metadata.get('source', 'unknown')}\")\n",[934,2425,2426,2437,2449,2461,2465,2470,2480,2491,2503,2507,2511,2516,2526,2546,2556,2560,2565,2570,2575,2580,2585,2589,2594,2599,2603,2611,2615,2620,2625,2630,2636,2647,2664,2689,2694,2699,2705,2716,2727,2743,2754,2770,2784,2789,2794,2800,2822,2841,2857,2876],{"__ignoreMap":11},[951,2427,2428,2430,2432,2434],{"class":953,"line":954},[951,2429,1091],{"class":1027},[951,2431,1981],{"class":1031},[951,2433,1028],{"class":1027},[951,2435,2436],{"class":1031}," ChatOpenAI\n",[951,2438,2439,2441,2444,2446],{"class":953,"line":977},[951,2440,1091],{"class":1027},[951,2442,2443],{"class":1031}," langchain.chains ",[951,2445,1028],{"class":1027},[951,2447,2448],{"class":1031}," RetrievalQA\n",[951,2450,2451,2453,2456,2458],{"class":953,"line":987},[951,2452,1091],{"class":1027},[951,2454,2455],{"class":1031}," langchain.prompts ",[951,2457,1028],{"class":1027},[951,2459,2460],{"class":1031}," PromptTemplate\n",[951,2462,2463],{"class":953,"line":1001},[951,2464,1055],{"emptyLinePlaceholder":1054},[951,2466,2467],{"class":953,"line":1063},[951,2468,2469],{"class":997},"# 初始化 LLM\n",[951,2471,2472,2475,2477],{"class":953,"line":1137},[951,2473,2474],{"class":1031},"llm ",[951,2476,1046],{"class":1027},[951,2478,2479],{"class":1031}," ChatOpenAI(\n",[951,2481,2482,2484,2486,2489],{"class":953,"line":1142},[951,2483,2022],{"class":1335},[951,2485,1046],{"class":1027},[951,2487,2488],{"class":961},"\"gpt-4o-mini\"",[951,2490,1330],{"class":1031},[951,2492,2493,2496,2498,2500],{"class":953,"line":1172},[951,2494,2495],{"class":1335},"    temperature",[951,2497,1046],{"class":1027},[951,2499,1190],{"class":1145},[951,2501,2502],{"class":997},"  # 降低随机性，使回答更稳定\n",[951,2504,2505],{"class":953,"line":1209},[951,2506,1124],{"class":1031},[951,2508,2509],{"class":953,"line":1236},[951,2510,1055],{"emptyLinePlaceholder":1054},[951,2512,2513],{"class":953,"line":1378},[951,2514,2515],{"class":997},"# 自定义 Prompt 模板\n",[951,2517,2518,2521,2523],{"class":953,"line":1645},[951,2519,2520],{"class":1031},"prompt_template ",[951,2522,1046],{"class":1027},[951,2524,2525],{"class":1031}," PromptTemplate(\n",[951,2527,2528,2531,2533,2535,2538,2540,2543],{"class":953,"line":1674},[951,2529,2530],{"class":1335},"    input_variables",[951,2532,1046],{"class":1027},[951,2534,1548],{"class":1031},[951,2536,2537],{"class":961},"\"context\"",[951,2539,1558],{"class":1031},[951,2541,2542],{"class":961},"\"question\"",[951,2544,2545],{"class":1031},"],\n",[951,2547,2548,2551,2553],{"class":953,"line":1837},[951,2549,2550],{"class":1335},"    template",[951,2552,1046],{"class":1027},[951,2554,2555],{"class":961},"\"\"\"你是一个专业的技术文档助手。请基于以下参考信息回答用户的问题。\n",[951,2557,2558],{"class":953,"line":1843},[951,2559,1055],{"emptyLinePlaceholder":1054},[951,2561,2562],{"class":953,"line":1849},[951,2563,2564],{"class":961},"要求：\n",[951,2566,2567],{"class":953,"line":1854},[951,2568,2569],{"class":961},"1. 只基于提供的参考信息回答，不要编造信息\n",[951,2571,2572],{"class":953,"line":1860},[951,2573,2574],{"class":961},"2. 如果参考信息中没有相关内容，请明确告知用户\n",[951,2576,2577],{"class":953,"line":1866},[951,2578,2579],{"class":961},"3. 回答要准确、条理清晰\n",[951,2581,2582],{"class":953,"line":1872},[951,2583,2584],{"class":961},"4. 适当引用原文内容\n",[951,2586,2587],{"class":953,"line":1877},[951,2588,1055],{"emptyLinePlaceholder":1054},[951,2590,2591],{"class":953,"line":1883},[951,2592,2593],{"class":961},"参考信息：\n",[951,2595,2596],{"class":953,"line":1889},[951,2597,2598],{"class":1145},"{context}\n",[951,2600,2601],{"class":953,"line":1895},[951,2602,1055],{"emptyLinePlaceholder":1054},[951,2604,2605,2608],{"class":953,"line":1900},[951,2606,2607],{"class":961},"用户问题：",[951,2609,2610],{"class":1145},"{question}\n",[951,2612,2613],{"class":953,"line":1911},[951,2614,1055],{"emptyLinePlaceholder":1054},[951,2616,2617],{"class":953,"line":1926},[951,2618,2619],{"class":961},"请回答：\"\"\"\n",[951,2621,2623],{"class":953,"line":2622},28,[951,2624,1124],{"class":1031},[951,2626,2628],{"class":953,"line":2627},29,[951,2629,1055],{"emptyLinePlaceholder":1054},[951,2631,2633],{"class":953,"line":2632},30,[951,2634,2635],{"class":997},"# 创建检索器\n",[951,2637,2639,2642,2644],{"class":953,"line":2638},31,[951,2640,2641],{"class":1031},"retriever ",[951,2643,1046],{"class":1027},[951,2645,2646],{"class":1031}," vectorstore.as_retriever(\n",[951,2648,2650,2653,2655,2658,2661],{"class":953,"line":2649},32,[951,2651,2652],{"class":1335},"    search_type",[951,2654,1046],{"class":1027},[951,2656,2657],{"class":961},"\"similarity\"",[951,2659,2660],{"class":1031},",   ",[951,2662,2663],{"class":997},"# 相似度搜索\n",[951,2665,2667,2670,2672,2674,2677,2680,2683,2686],{"class":953,"line":2666},33,[951,2668,2669],{"class":1335},"    search_kwargs",[951,2671,1046],{"class":1027},[951,2673,1184],{"class":1031},[951,2675,2676],{"class":961},"\"k\"",[951,2678,2679],{"class":1031},": ",[951,2681,2682],{"class":1145},"4",[951,2684,2685],{"class":1031},"}      ",[951,2687,2688],{"class":997},"# 返回 top-4 个结果\n",[951,2690,2692],{"class":953,"line":2691},34,[951,2693,1124],{"class":1031},[951,2695,2697],{"class":953,"line":2696},35,[951,2698,1055],{"emptyLinePlaceholder":1054},[951,2700,2702],{"class":953,"line":2701},36,[951,2703,2704],{"class":997},"# 创建 RAG 链\n",[951,2706,2708,2711,2713],{"class":953,"line":2707},37,[951,2709,2710],{"class":1031},"qa_chain ",[951,2712,1046],{"class":1027},[951,2714,2715],{"class":1031}," RetrievalQA.from_chain_type(\n",[951,2717,2719,2722,2724],{"class":953,"line":2718},38,[951,2720,2721],{"class":1335},"    llm",[951,2723,1046],{"class":1027},[951,2725,2726],{"class":1031},"llm,\n",[951,2728,2730,2733,2735,2738,2740],{"class":953,"line":2729},39,[951,2731,2732],{"class":1335},"    chain_type",[951,2734,1046],{"class":1027},[951,2736,2737],{"class":961},"\"stuff\"",[951,2739,2168],{"class":1031},[951,2741,2742],{"class":997},"# 将所有检索结果拼接到 prompt 中\n",[951,2744,2746,2749,2751],{"class":953,"line":2745},40,[951,2747,2748],{"class":1335},"    retriever",[951,2750,1046],{"class":1027},[951,2752,2753],{"class":1031},"retriever,\n",[951,2755,2757,2760,2762,2764,2767],{"class":953,"line":2756},41,[951,2758,2759],{"class":1335},"    chain_type_kwargs",[951,2761,1046],{"class":1027},[951,2763,1184],{"class":1031},[951,2765,2766],{"class":961},"\"prompt\"",[951,2768,2769],{"class":1031},": prompt_template},\n",[951,2771,2773,2776,2778,2781],{"class":953,"line":2772},42,[951,2774,2775],{"class":1335},"    return_source_documents",[951,2777,1046],{"class":1027},[951,2779,2780],{"class":1145},"True",[951,2782,2783],{"class":997},"  # 返回源文档\n",[951,2785,2787],{"class":953,"line":2786},43,[951,2788,1124],{"class":1031},[951,2790,2792],{"class":953,"line":2791},44,[951,2793,1055],{"emptyLinePlaceholder":1054},[951,2795,2797],{"class":953,"line":2796},45,[951,2798,2799],{"class":997},"# 提问\n",[951,2801,2803,2806,2808,2811,2814,2816,2819],{"class":953,"line":2802},46,[951,2804,2805],{"class":1031},"response ",[951,2807,1046],{"class":1027},[951,2809,2810],{"class":1031}," qa_chain.invoke({",[951,2812,2813],{"class":961},"\"query\"",[951,2815,2679],{"class":1031},[951,2817,2818],{"class":961},"\"Kubernetes 中 Pod 和 Service 的区别是什么？\"",[951,2820,2821],{"class":1031},"})\n",[951,2823,2825,2827,2829,2832,2835,2838],{"class":953,"line":2824},47,[951,2826,1146],{"class":1145},[951,2828,1149],{"class":1031},[951,2830,2831],{"class":961},"\"回答:\"",[951,2833,2834],{"class":1031},", response[",[951,2836,2837],{"class":961},"\"result\"",[951,2839,2840],{"class":1031},"])\n",[951,2842,2844,2846,2848,2850,2852,2855],{"class":953,"line":2843},48,[951,2845,1146],{"class":1145},[951,2847,1149],{"class":1031},[951,2849,1204],{"class":961},[951,2851,1563],{"class":1145},[951,2853,2854],{"class":961},"参考文档:\"",[951,2856,1124],{"class":1031},[951,2858,2860,2862,2865,2867,2870,2873],{"class":953,"line":2859},49,[951,2861,1914],{"class":1027},[951,2863,2864],{"class":1031}," doc ",[951,2866,1920],{"class":1027},[951,2868,2869],{"class":1031}," response[",[951,2871,2872],{"class":961},"\"source_documents\"",[951,2874,2875],{"class":1031},"]:\n",[951,2877,2879,2881,2883,2885,2888,2890,2892,2894,2896,2898,2900,2902,2904],{"class":953,"line":2878},50,[951,2880,1929],{"class":1145},[951,2882,1149],{"class":1031},[951,2884,1152],{"class":1027},[951,2886,2887],{"class":961},"\"  - ",[951,2889,1184],{"class":1145},[951,2891,2360],{"class":1031},[951,2893,2363],{"class":961},[951,2895,1558],{"class":1031},[951,2897,2368],{"class":961},[951,2899,2371],{"class":1031},[951,2901,1164],{"class":1145},[951,2903,1204],{"class":961},[951,2905,1124],{"class":1031},[940,2907,2909],{"id":2908},"使用-lcellangchain-expression-language","使用 LCEL（LangChain Expression Language）",[896,2911,2912],{},"LangChain 的新版推荐使用 LCEL 方式构建链，更加灵活：",[926,2914,2916],{"className":1018,"code":2915,"language":1020,"meta":11,"style":11},"from langchain_openai import ChatOpenAI\nfrom langchain_core.prompts import ChatPromptTemplate\nfrom langchain_core.runnables import RunnablePassthrough\nfrom langchain_core.output_parsers import StrOutputParser\n\n# 定义 Prompt\nprompt = ChatPromptTemplate.from_template(\"\"\"基于以下参考信息回答问题。\n如果无法从参考信息中找到答案，请说明。\n\n参考信息：\n{context}\n\n问题：{question}\n\n回答：\"\"\")\n\n# 辅助函数：格式化检索结果\ndef format_docs(docs):\n    return \"\\n\\n\".join(doc.page_content for doc in docs)\n\n# 使用 LCEL 构建 RAG 链\nrag_chain = (\n    {\n        \"context\": retriever | format_docs,\n        \"question\": RunnablePassthrough()\n    }\n    | prompt\n    | llm\n    | StrOutputParser()\n)\n\n# 调用\nanswer = rag_chain.invoke(\"如何创建一个 Kubernetes Deployment？\")\nprint(answer)\n",[934,2917,2918,2928,2940,2952,2964,2968,2973,2986,2991,2995,2999,3003,3007,3014,3018,3025,3029,3034,3045,3069,3073,3078,3088,3093,3107,3115,3120,3128,3135,3142,3146,3150,3155,3170],{"__ignoreMap":11},[951,2919,2920,2922,2924,2926],{"class":953,"line":954},[951,2921,1091],{"class":1027},[951,2923,1981],{"class":1031},[951,2925,1028],{"class":1027},[951,2927,2436],{"class":1031},[951,2929,2930,2932,2935,2937],{"class":953,"line":977},[951,2931,1091],{"class":1027},[951,2933,2934],{"class":1031}," langchain_core.prompts ",[951,2936,1028],{"class":1027},[951,2938,2939],{"class":1031}," ChatPromptTemplate\n",[951,2941,2942,2944,2947,2949],{"class":953,"line":987},[951,2943,1091],{"class":1027},[951,2945,2946],{"class":1031}," langchain_core.runnables ",[951,2948,1028],{"class":1027},[951,2950,2951],{"class":1031}," RunnablePassthrough\n",[951,2953,2954,2956,2959,2961],{"class":953,"line":1001},[951,2955,1091],{"class":1027},[951,2957,2958],{"class":1031}," langchain_core.output_parsers ",[951,2960,1028],{"class":1027},[951,2962,2963],{"class":1031}," StrOutputParser\n",[951,2965,2966],{"class":953,"line":1063},[951,2967,1055],{"emptyLinePlaceholder":1054},[951,2969,2970],{"class":953,"line":1137},[951,2971,2972],{"class":997},"# 定义 Prompt\n",[951,2974,2975,2978,2980,2983],{"class":953,"line":1142},[951,2976,2977],{"class":1031},"prompt ",[951,2979,1046],{"class":1027},[951,2981,2982],{"class":1031}," ChatPromptTemplate.from_template(",[951,2984,2985],{"class":961},"\"\"\"基于以下参考信息回答问题。\n",[951,2987,2988],{"class":953,"line":1172},[951,2989,2990],{"class":961},"如果无法从参考信息中找到答案，请说明。\n",[951,2992,2993],{"class":953,"line":1209},[951,2994,1055],{"emptyLinePlaceholder":1054},[951,2996,2997],{"class":953,"line":1236},[951,2998,2593],{"class":961},[951,3000,3001],{"class":953,"line":1378},[951,3002,2598],{"class":1145},[951,3004,3005],{"class":953,"line":1645},[951,3006,1055],{"emptyLinePlaceholder":1054},[951,3008,3009,3012],{"class":953,"line":1674},[951,3010,3011],{"class":961},"问题：",[951,3013,2610],{"class":1145},[951,3015,3016],{"class":953,"line":1837},[951,3017,1055],{"emptyLinePlaceholder":1054},[951,3019,3020,3023],{"class":953,"line":1843},[951,3021,3022],{"class":961},"回答：\"\"\"",[951,3024,1124],{"class":1031},[951,3026,3027],{"class":953,"line":1849},[951,3028,1055],{"emptyLinePlaceholder":1054},[951,3030,3031],{"class":953,"line":1854},[951,3032,3033],{"class":997},"# 辅助函数：格式化检索结果\n",[951,3035,3036,3039,3042],{"class":953,"line":1860},[951,3037,3038],{"class":1027},"def",[951,3040,3041],{"class":957}," format_docs",[951,3043,3044],{"class":1031},"(docs):\n",[951,3046,3047,3050,3053,3055,3057,3060,3062,3064,3066],{"class":953,"line":1866},[951,3048,3049],{"class":1027},"    return",[951,3051,3052],{"class":961}," \"",[951,3054,1553],{"class":1145},[951,3056,1204],{"class":961},[951,3058,3059],{"class":1031},".join(doc.page_content ",[951,3061,1914],{"class":1027},[951,3063,2864],{"class":1031},[951,3065,1920],{"class":1027},[951,3067,3068],{"class":1031}," docs)\n",[951,3070,3071],{"class":953,"line":1872},[951,3072,1055],{"emptyLinePlaceholder":1054},[951,3074,3075],{"class":953,"line":1877},[951,3076,3077],{"class":997},"# 使用 LCEL 构建 RAG 链\n",[951,3079,3080,3083,3085],{"class":953,"line":1883},[951,3081,3082],{"class":1031},"rag_chain ",[951,3084,1046],{"class":1027},[951,3086,3087],{"class":1031}," (\n",[951,3089,3090],{"class":953,"line":1889},[951,3091,3092],{"class":1031},"    {\n",[951,3094,3095,3098,3101,3104],{"class":953,"line":1895},[951,3096,3097],{"class":961},"        \"context\"",[951,3099,3100],{"class":1031},": retriever ",[951,3102,3103],{"class":1027},"|",[951,3105,3106],{"class":1031}," format_docs,\n",[951,3108,3109,3112],{"class":953,"line":1900},[951,3110,3111],{"class":961},"        \"question\"",[951,3113,3114],{"class":1031},": RunnablePassthrough()\n",[951,3116,3117],{"class":953,"line":1911},[951,3118,3119],{"class":1031},"    }\n",[951,3121,3122,3125],{"class":953,"line":1926},[951,3123,3124],{"class":1027},"    |",[951,3126,3127],{"class":1031}," prompt\n",[951,3129,3130,3132],{"class":953,"line":2622},[951,3131,3124],{"class":1027},[951,3133,3134],{"class":1031}," llm\n",[951,3136,3137,3139],{"class":953,"line":2627},[951,3138,3124],{"class":1027},[951,3140,3141],{"class":1031}," StrOutputParser()\n",[951,3143,3144],{"class":953,"line":2632},[951,3145,1124],{"class":1031},[951,3147,3148],{"class":953,"line":2638},[951,3149,1055],{"emptyLinePlaceholder":1054},[951,3151,3152],{"class":953,"line":2649},[951,3153,3154],{"class":997},"# 调用\n",[951,3156,3157,3160,3162,3165,3168],{"class":953,"line":2666},[951,3158,3159],{"class":1031},"answer ",[951,3161,1046],{"class":1027},[951,3163,3164],{"class":1031}," rag_chain.invoke(",[951,3166,3167],{"class":961},"\"如何创建一个 Kubernetes Deployment？\"",[951,3169,1124],{"class":1031},[951,3171,3172,3174],{"class":953,"line":2691},[951,3173,1146],{"class":1145},[951,3175,3176],{"class":1031},"(answer)\n",[940,3178,3179],{"id":3179},"支持对话历史",[926,3181,3183],{"className":1018,"code":3182,"language":1020,"meta":11,"style":11},"from langchain.chains import ConversationalRetrievalChain\nfrom langchain.memory import ConversationBufferMemory\n\nmemory = ConversationBufferMemory(\n    memory_key=\"chat_history\",\n    return_messages=True,\n    output_key=\"answer\"\n)\n\nconv_chain = ConversationalRetrievalChain.from_llm(\n    llm=llm,\n    retriever=retriever,\n    memory=memory,\n    return_source_documents=True\n)\n\n# 多轮对话\nresponse1 = conv_chain.invoke({\"question\": \"什么是 Pod？\"})\nprint(\"回答1:\", response1[\"answer\"])\n\nresponse2 = conv_chain.invoke({\"question\": \"它和 Deployment 有什么关系？\"})\nprint(\"回答2:\", response2[\"answer\"])  # 能理解\"它\"指的是 Pod\n",[934,3184,3185,3196,3208,3212,3222,3234,3245,3255,3259,3263,3273,3281,3289,3299,3307,3311,3315,3320,3339,3356,3360,3378],{"__ignoreMap":11},[951,3186,3187,3189,3191,3193],{"class":953,"line":954},[951,3188,1091],{"class":1027},[951,3190,2443],{"class":1031},[951,3192,1028],{"class":1027},[951,3194,3195],{"class":1031}," ConversationalRetrievalChain\n",[951,3197,3198,3200,3203,3205],{"class":953,"line":977},[951,3199,1091],{"class":1027},[951,3201,3202],{"class":1031}," langchain.memory ",[951,3204,1028],{"class":1027},[951,3206,3207],{"class":1031}," ConversationBufferMemory\n",[951,3209,3210],{"class":953,"line":987},[951,3211,1055],{"emptyLinePlaceholder":1054},[951,3213,3214,3217,3219],{"class":953,"line":1001},[951,3215,3216],{"class":1031},"memory ",[951,3218,1046],{"class":1027},[951,3220,3221],{"class":1031}," ConversationBufferMemory(\n",[951,3223,3224,3227,3229,3232],{"class":953,"line":1063},[951,3225,3226],{"class":1335},"    memory_key",[951,3228,1046],{"class":1027},[951,3230,3231],{"class":961},"\"chat_history\"",[951,3233,1330],{"class":1031},[951,3235,3236,3239,3241,3243],{"class":953,"line":1137},[951,3237,3238],{"class":1335},"    return_messages",[951,3240,1046],{"class":1027},[951,3242,2780],{"class":1145},[951,3244,1330],{"class":1031},[951,3246,3247,3250,3252],{"class":953,"line":1142},[951,3248,3249],{"class":1335},"    output_key",[951,3251,1046],{"class":1027},[951,3253,3254],{"class":961},"\"answer\"\n",[951,3256,3257],{"class":953,"line":1172},[951,3258,1124],{"class":1031},[951,3260,3261],{"class":953,"line":1209},[951,3262,1055],{"emptyLinePlaceholder":1054},[951,3264,3265,3268,3270],{"class":953,"line":1236},[951,3266,3267],{"class":1031},"conv_chain ",[951,3269,1046],{"class":1027},[951,3271,3272],{"class":1031}," ConversationalRetrievalChain.from_llm(\n",[951,3274,3275,3277,3279],{"class":953,"line":1378},[951,3276,2721],{"class":1335},[951,3278,1046],{"class":1027},[951,3280,2726],{"class":1031},[951,3282,3283,3285,3287],{"class":953,"line":1645},[951,3284,2748],{"class":1335},[951,3286,1046],{"class":1027},[951,3288,2753],{"class":1031},[951,3290,3291,3294,3296],{"class":953,"line":1674},[951,3292,3293],{"class":1335},"    memory",[951,3295,1046],{"class":1027},[951,3297,3298],{"class":1031},"memory,\n",[951,3300,3301,3303,3305],{"class":953,"line":1837},[951,3302,2775],{"class":1335},[951,3304,1046],{"class":1027},[951,3306,1363],{"class":1145},[951,3308,3309],{"class":953,"line":1843},[951,3310,1124],{"class":1031},[951,3312,3313],{"class":953,"line":1849},[951,3314,1055],{"emptyLinePlaceholder":1054},[951,3316,3317],{"class":953,"line":1854},[951,3318,3319],{"class":997},"# 多轮对话\n",[951,3321,3322,3325,3327,3330,3332,3334,3337],{"class":953,"line":1860},[951,3323,3324],{"class":1031},"response1 ",[951,3326,1046],{"class":1027},[951,3328,3329],{"class":1031}," conv_chain.invoke({",[951,3331,2542],{"class":961},[951,3333,2679],{"class":1031},[951,3335,3336],{"class":961},"\"什么是 Pod？\"",[951,3338,2821],{"class":1031},[951,3340,3341,3343,3345,3348,3351,3354],{"class":953,"line":1866},[951,3342,1146],{"class":1145},[951,3344,1149],{"class":1031},[951,3346,3347],{"class":961},"\"回答1:\"",[951,3349,3350],{"class":1031},", response1[",[951,3352,3353],{"class":961},"\"answer\"",[951,3355,2840],{"class":1031},[951,3357,3358],{"class":953,"line":1872},[951,3359,1055],{"emptyLinePlaceholder":1054},[951,3361,3362,3365,3367,3369,3371,3373,3376],{"class":953,"line":1877},[951,3363,3364],{"class":1031},"response2 ",[951,3366,1046],{"class":1027},[951,3368,3329],{"class":1031},[951,3370,2542],{"class":961},[951,3372,2679],{"class":1031},[951,3374,3375],{"class":961},"\"它和 Deployment 有什么关系？\"",[951,3377,2821],{"class":1031},[951,3379,3380,3382,3384,3387,3390,3392,3395],{"class":953,"line":1883},[951,3381,1146],{"class":1145},[951,3383,1149],{"class":1031},[951,3385,3386],{"class":961},"\"回答2:\"",[951,3388,3389],{"class":1031},", response2[",[951,3391,3353],{"class":961},[951,3393,3394],{"class":1031},"])  ",[951,3396,3397],{"class":997},"# 能理解\"它\"指的是 Pod\n",[892,3399,3400],{"id":3400},"完整示例代码",[896,3402,3403],{},"以下是一个完整的、可直接运行的 RAG 系统实现：",[926,3405,3407],{"className":1018,"code":3406,"language":1020,"meta":11,"style":11},"\"\"\"\n完整的 RAG 文档问答系统\n使用 LangChain + ChromaDB + OpenAI\n\"\"\"\nimport os\nfrom langchain_community.document_loaders import PyPDFLoader, DirectoryLoader\nfrom langchain.text_splitter import RecursiveCharacterTextSplitter\nfrom langchain_openai import OpenAIEmbeddings, ChatOpenAI\nfrom langchain_chroma import Chroma\nfrom langchain_core.prompts import ChatPromptTemplate\nfrom langchain_core.runnables import RunnablePassthrough\nfrom langchain_core.output_parsers import StrOutputParser\n\n# ===== 配置 =====\nos.environ[\"OPENAI_API_KEY\"] = \"sk-your-api-key\"\nDOCS_DIR = \".\u002Fdocs\"\nCHROMA_DIR = \".\u002Fchroma_db\"\nCOLLECTION_NAME = \"knowledge_base\"\n\n# ===== 1. 文档加载 =====\ndef load_documents(docs_dir: str):\n    \"\"\"加载目录下的所有 PDF 文档\"\"\"\n    loader = DirectoryLoader(\n        docs_dir,\n        glob=\"**\u002F*.pdf\",\n        loader_cls=PyPDFLoader,\n        show_progress=True\n    )\n    return loader.load()\n\n# ===== 2. 文本分割 =====\ndef split_documents(documents):\n    \"\"\"将文档分割为小块\"\"\"\n    splitter = RecursiveCharacterTextSplitter(\n        chunk_size=500,\n        chunk_overlap=100,\n        separators=[\"\\n\\n\", \"\\n\", \"。\", \"！\", \"？\", \".\", \" \", \"\"]\n    )\n    return splitter.split_documents(documents)\n\n# ===== 3. 向量存储 =====\ndef create_vectorstore(chunks, persist_dir: str):\n    \"\"\"创建并持久化向量存储\"\"\"\n    embeddings = OpenAIEmbeddings(model=\"text-embedding-3-small\")\n    vectorstore = Chroma.from_documents(\n        documents=chunks,\n        embedding=embeddings,\n        persist_directory=persist_dir,\n        collection_name=COLLECTION_NAME\n    )\n    return vectorstore\n\ndef load_vectorstore(persist_dir: str):\n    \"\"\"加载已有的向量存储\"\"\"\n    embeddings = OpenAIEmbeddings(model=\"text-embedding-3-small\")\n    return Chroma(\n        persist_directory=persist_dir,\n        embedding_function=embeddings,\n        collection_name=COLLECTION_NAME\n    )\n\n# ===== 4. RAG 链 =====\ndef create_rag_chain(vectorstore):\n    \"\"\"创建 RAG 问答链\"\"\"\n    retriever = vectorstore.as_retriever(\n        search_type=\"similarity\",\n        search_kwargs={\"k\": 4}\n    )\n\n    llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n\n    prompt = ChatPromptTemplate.from_template(\n        \"\"\"你是一个专业的技术文档助手。请基于以下参考信息回答用户的问题。\n\n要求：\n1. 只基于提供的参考信息回答，不要编造信息\n2. 如果参考信息不足以回答问题，请明确告知\n3. 回答要准确、有条理\n\n参考信息：\n{context}\n\n用户问题：{question}\n\n请回答：\"\"\"\n    )\n\n    def format_docs(docs):\n        return \"\\n\\n---\\n\\n\".join(doc.page_content for doc in docs)\n\n    chain = (\n        {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n        | prompt\n        | llm\n        | StrOutputParser()\n    )\n    return chain\n\n# ===== 主流程 =====\ndef build_knowledge_base():\n    \"\"\"构建知识库（首次运行）\"\"\"\n    print(\"正在加载文档...\")\n    documents = load_documents(DOCS_DIR)\n    print(f\"加载了 {len(documents)} 个文档片段\")\n\n    print(\"正在分割文本...\")\n    chunks = split_documents(documents)\n    print(f\"分割为 {len(chunks)} 个文本块\")\n\n    print(\"正在创建向量存储...\")\n    vectorstore = create_vectorstore(chunks, CHROMA_DIR)\n    print(f\"向量存储创建完成，共 {vectorstore._collection.count()} 条记录\")\n    return vectorstore\n\ndef chat():\n    \"\"\"交互式问答\"\"\"\n    print(\"正在加载知识库...\")\n    vectorstore = load_vectorstore(CHROMA_DIR)\n    chain = create_rag_chain(vectorstore)\n\n    print(\"知识库已就绪！输入问题开始对话（输入 'quit' 退出）\\n\")\n    while True:\n        question = input(\"你: \").strip()\n        if question.lower() in (\"quit\", \"exit\", \"q\"):\n            break\n        if not question:\n            continue\n\n        answer = chain.invoke(question)\n        print(f\"\\n助手: {answer}\\n\")\n\nif __name__ == \"__main__\":\n    import sys\n    if len(sys.argv) > 1 and sys.argv[1] == \"build\":\n        build_knowledge_base()\n    else:\n        chat()\n",[934,3408,3409,3413,3418,3423,3427,3433,3444,3454,3465,3475,3485,3495,3505,3509,3514,3526,3537,3547,3557,3561,3566,3582,3587,3596,3601,3612,3621,3630,3635,3641,3645,3650,3660,3665,3674,3685,3696,3745,3749,3756,3760,3765,3779,3784,3803,3812,3821,3830,3840,3850,3854,3862,3867,3882,3888,3905,3912,3921,3931,3940,3945,3950,3956,3967,3973,3983,3995,4014,4019,4024,4052,4057,4068,4074,4079,4084,4089,4095,4101,4106,4111,4116,4121,4128,4133,4138,4143,4148,4158,4185,4190,4200,4220,4228,4235,4242,4247,4255,4260,4266,4277,4283,4295,4310,4331,4336,4348,4359,4382,4387,4399,4413,4436,4443,4448,4458,4464,4476,4490,4500,4505,4521,4533,4552,4581,4587,4598,4604,4609,4620,4648,4653,4670,4679,4716,4722,4730],{"__ignoreMap":11},[951,3410,3411],{"class":953,"line":954},[951,3412,1892],{"class":961},[951,3414,3415],{"class":953,"line":977},[951,3416,3417],{"class":961},"完整的 RAG 文档问答系统\n",[951,3419,3420],{"class":953,"line":987},[951,3421,3422],{"class":961},"使用 LangChain + ChromaDB + OpenAI\n",[951,3424,3425],{"class":953,"line":1001},[951,3426,1892],{"class":961},[951,3428,3429,3431],{"class":953,"line":1063},[951,3430,1028],{"class":1027},[951,3432,1032],{"class":1031},[951,3434,3435,3437,3439,3441],{"class":953,"line":1137},[951,3436,1091],{"class":1027},[951,3438,1094],{"class":1031},[951,3440,1028],{"class":1027},[951,3442,3443],{"class":1031}," PyPDFLoader, DirectoryLoader\n",[951,3445,3446,3448,3450,3452],{"class":953,"line":1142},[951,3447,1091],{"class":1027},[951,3449,1475],{"class":1031},[951,3451,1028],{"class":1027},[951,3453,1480],{"class":1031},[951,3455,3456,3458,3460,3462],{"class":953,"line":1172},[951,3457,1091],{"class":1027},[951,3459,1981],{"class":1031},[951,3461,1028],{"class":1027},[951,3463,3464],{"class":1031}," OpenAIEmbeddings, ChatOpenAI\n",[951,3466,3467,3469,3471,3473],{"class":953,"line":1209},[951,3468,1091],{"class":1027},[951,3470,1993],{"class":1031},[951,3472,1028],{"class":1027},[951,3474,1998],{"class":1031},[951,3476,3477,3479,3481,3483],{"class":953,"line":1236},[951,3478,1091],{"class":1027},[951,3480,2934],{"class":1031},[951,3482,1028],{"class":1027},[951,3484,2939],{"class":1031},[951,3486,3487,3489,3491,3493],{"class":953,"line":1378},[951,3488,1091],{"class":1027},[951,3490,2946],{"class":1031},[951,3492,1028],{"class":1027},[951,3494,2951],{"class":1031},[951,3496,3497,3499,3501,3503],{"class":953,"line":1645},[951,3498,1091],{"class":1027},[951,3500,2958],{"class":1031},[951,3502,1028],{"class":1027},[951,3504,2963],{"class":1031},[951,3506,3507],{"class":953,"line":1674},[951,3508,1055],{"emptyLinePlaceholder":1054},[951,3510,3511],{"class":953,"line":1837},[951,3512,3513],{"class":997},"# ===== 配置 =====\n",[951,3515,3516,3518,3520,3522,3524],{"class":953,"line":1843},[951,3517,1037],{"class":1031},[951,3519,1040],{"class":961},[951,3521,1043],{"class":1031},[951,3523,1046],{"class":1027},[951,3525,1049],{"class":961},[951,3527,3528,3531,3534],{"class":953,"line":1849},[951,3529,3530],{"class":1145},"DOCS_DIR",[951,3532,3533],{"class":1027}," =",[951,3535,3536],{"class":961}," \".\u002Fdocs\"\n",[951,3538,3539,3542,3544],{"class":953,"line":1854},[951,3540,3541],{"class":1145},"CHROMA_DIR",[951,3543,3533],{"class":1027},[951,3545,3546],{"class":961}," \".\u002Fchroma_db\"\n",[951,3548,3549,3552,3554],{"class":953,"line":1860},[951,3550,3551],{"class":1145},"COLLECTION_NAME",[951,3553,3533],{"class":1027},[951,3555,3556],{"class":961}," \"knowledge_base\"\n",[951,3558,3559],{"class":953,"line":1866},[951,3560,1055],{"emptyLinePlaceholder":1054},[951,3562,3563],{"class":953,"line":1872},[951,3564,3565],{"class":997},"# ===== 1. 文档加载 =====\n",[951,3567,3568,3570,3573,3576,3579],{"class":953,"line":1877},[951,3569,3038],{"class":1027},[951,3571,3572],{"class":957}," load_documents",[951,3574,3575],{"class":1031},"(docs_dir: ",[951,3577,3578],{"class":1145},"str",[951,3580,3581],{"class":1031},"):\n",[951,3583,3584],{"class":953,"line":1883},[951,3585,3586],{"class":961},"    \"\"\"加载目录下的所有 PDF 文档\"\"\"\n",[951,3588,3589,3592,3594],{"class":953,"line":1889},[951,3590,3591],{"class":1031},"    loader ",[951,3593,1046],{"class":1027},[951,3595,1322],{"class":1031},[951,3597,3598],{"class":953,"line":1895},[951,3599,3600],{"class":1031},"        docs_dir,\n",[951,3602,3603,3606,3608,3610],{"class":953,"line":1900},[951,3604,3605],{"class":1335},"        glob",[951,3607,1046],{"class":1027},[951,3609,1341],{"class":961},[951,3611,1330],{"class":1031},[951,3613,3614,3617,3619],{"class":953,"line":1911},[951,3615,3616],{"class":1335},"        loader_cls",[951,3618,1046],{"class":1027},[951,3620,1353],{"class":1031},[951,3622,3623,3626,3628],{"class":953,"line":1926},[951,3624,3625],{"class":1335},"        show_progress",[951,3627,1046],{"class":1027},[951,3629,1363],{"class":1145},[951,3631,3632],{"class":953,"line":2622},[951,3633,3634],{"class":1031},"    )\n",[951,3636,3637,3639],{"class":953,"line":2627},[951,3638,3049],{"class":1027},[951,3640,1134],{"class":1031},[951,3642,3643],{"class":953,"line":2632},[951,3644,1055],{"emptyLinePlaceholder":1054},[951,3646,3647],{"class":953,"line":2638},[951,3648,3649],{"class":997},"# ===== 2. 文本分割 =====\n",[951,3651,3652,3654,3657],{"class":953,"line":2649},[951,3653,3038],{"class":1027},[951,3655,3656],{"class":957}," split_documents",[951,3658,3659],{"class":1031},"(documents):\n",[951,3661,3662],{"class":953,"line":2666},[951,3663,3664],{"class":961},"    \"\"\"将文档分割为小块\"\"\"\n",[951,3666,3667,3670,3672],{"class":953,"line":2691},[951,3668,3669],{"class":1031},"    splitter ",[951,3671,1046],{"class":1027},[951,3673,1494],{"class":1031},[951,3675,3676,3679,3681,3683],{"class":953,"line":2696},[951,3677,3678],{"class":1335},"        chunk_size",[951,3680,1046],{"class":1027},[951,3682,1504],{"class":1145},[951,3684,1330],{"class":1031},[951,3686,3687,3690,3692,3694],{"class":953,"line":2701},[951,3688,3689],{"class":1335},"        chunk_overlap",[951,3691,1046],{"class":1027},[951,3693,1520],{"class":1145},[951,3695,1330],{"class":1031},[951,3697,3698,3701,3703,3705,3707,3709,3711,3713,3715,3717,3719,3721,3723,3725,3727,3729,3731,3733,3735,3737,3739,3741,3743],{"class":953,"line":2707},[951,3699,3700],{"class":1335},"        separators",[951,3702,1046],{"class":1027},[951,3704,1548],{"class":1031},[951,3706,1204],{"class":961},[951,3708,1553],{"class":1145},[951,3710,1204],{"class":961},[951,3712,1558],{"class":1031},[951,3714,1204],{"class":961},[951,3716,1563],{"class":1145},[951,3718,1204],{"class":961},[951,3720,1558],{"class":1031},[951,3722,1570],{"class":961},[951,3724,1558],{"class":1031},[951,3726,1575],{"class":961},[951,3728,1558],{"class":1031},[951,3730,1580],{"class":961},[951,3732,1558],{"class":1031},[951,3734,1585],{"class":961},[951,3736,1558],{"class":1031},[951,3738,1590],{"class":961},[951,3740,1558],{"class":1031},[951,3742,1595],{"class":961},[951,3744,1789],{"class":1031},[951,3746,3747],{"class":953,"line":2718},[951,3748,3634],{"class":1031},[951,3750,3751,3753],{"class":953,"line":2729},[951,3752,3049],{"class":1027},[951,3754,3755],{"class":1031}," splitter.split_documents(documents)\n",[951,3757,3758],{"class":953,"line":2745},[951,3759,1055],{"emptyLinePlaceholder":1054},[951,3761,3762],{"class":953,"line":2756},[951,3763,3764],{"class":997},"# ===== 3. 向量存储 =====\n",[951,3766,3767,3769,3772,3775,3777],{"class":953,"line":2772},[951,3768,3038],{"class":1027},[951,3770,3771],{"class":957}," create_vectorstore",[951,3773,3774],{"class":1031},"(chunks, persist_dir: ",[951,3776,3578],{"class":1145},[951,3778,3581],{"class":1031},[951,3780,3781],{"class":953,"line":2786},[951,3782,3783],{"class":961},"    \"\"\"创建并持久化向量存储\"\"\"\n",[951,3785,3786,3789,3791,3794,3797,3799,3801],{"class":953,"line":2791},[951,3787,3788],{"class":1031},"    embeddings ",[951,3790,1046],{"class":1027},[951,3792,3793],{"class":1031}," OpenAIEmbeddings(",[951,3795,3796],{"class":1335},"model",[951,3798,1046],{"class":1027},[951,3800,2027],{"class":961},[951,3802,1124],{"class":1031},[951,3804,3805,3808,3810],{"class":953,"line":2796},[951,3806,3807],{"class":1031},"    vectorstore ",[951,3809,1046],{"class":1027},[951,3811,2057],{"class":1031},[951,3813,3814,3817,3819],{"class":953,"line":2802},[951,3815,3816],{"class":1335},"        documents",[951,3818,1046],{"class":1027},[951,3820,2067],{"class":1031},[951,3822,3823,3826,3828],{"class":953,"line":2824},[951,3824,3825],{"class":1335},"        embedding",[951,3827,1046],{"class":1027},[951,3829,2077],{"class":1031},[951,3831,3832,3835,3837],{"class":953,"line":2843},[951,3833,3834],{"class":1335},"        persist_directory",[951,3836,1046],{"class":1027},[951,3838,3839],{"class":1031},"persist_dir,\n",[951,3841,3842,3845,3847],{"class":953,"line":2859},[951,3843,3844],{"class":1335},"        collection_name",[951,3846,1046],{"class":1027},[951,3848,3849],{"class":1145},"COLLECTION_NAME\n",[951,3851,3852],{"class":953,"line":2878},[951,3853,3634],{"class":1031},[951,3855,3857,3859],{"class":953,"line":3856},51,[951,3858,3049],{"class":1027},[951,3860,3861],{"class":1031}," vectorstore\n",[951,3863,3865],{"class":953,"line":3864},52,[951,3866,1055],{"emptyLinePlaceholder":1054},[951,3868,3870,3872,3875,3878,3880],{"class":953,"line":3869},53,[951,3871,3038],{"class":1027},[951,3873,3874],{"class":957}," load_vectorstore",[951,3876,3877],{"class":1031},"(persist_dir: ",[951,3879,3578],{"class":1145},[951,3881,3581],{"class":1031},[951,3883,3885],{"class":953,"line":3884},54,[951,3886,3887],{"class":961},"    \"\"\"加载已有的向量存储\"\"\"\n",[951,3889,3891,3893,3895,3897,3899,3901,3903],{"class":953,"line":3890},55,[951,3892,3788],{"class":1031},[951,3894,1046],{"class":1027},[951,3896,3793],{"class":1031},[951,3898,3796],{"class":1335},[951,3900,1046],{"class":1027},[951,3902,2027],{"class":961},[951,3904,1124],{"class":1031},[951,3906,3908,3910],{"class":953,"line":3907},56,[951,3909,3049],{"class":1027},[951,3911,2201],{"class":1031},[951,3913,3915,3917,3919],{"class":953,"line":3914},57,[951,3916,3834],{"class":1335},[951,3918,1046],{"class":1027},[951,3920,3839],{"class":1031},[951,3922,3924,3927,3929],{"class":953,"line":3923},58,[951,3925,3926],{"class":1335},"        embedding_function",[951,3928,1046],{"class":1027},[951,3930,2077],{"class":1031},[951,3932,3934,3936,3938],{"class":953,"line":3933},59,[951,3935,3844],{"class":1335},[951,3937,1046],{"class":1027},[951,3939,3849],{"class":1145},[951,3941,3943],{"class":953,"line":3942},60,[951,3944,3634],{"class":1031},[951,3946,3948],{"class":953,"line":3947},61,[951,3949,1055],{"emptyLinePlaceholder":1054},[951,3951,3953],{"class":953,"line":3952},62,[951,3954,3955],{"class":997},"# ===== 4. RAG 链 =====\n",[951,3957,3959,3961,3964],{"class":953,"line":3958},63,[951,3960,3038],{"class":1027},[951,3962,3963],{"class":957}," create_rag_chain",[951,3965,3966],{"class":1031},"(vectorstore):\n",[951,3968,3970],{"class":953,"line":3969},64,[951,3971,3972],{"class":961},"    \"\"\"创建 RAG 问答链\"\"\"\n",[951,3974,3976,3979,3981],{"class":953,"line":3975},65,[951,3977,3978],{"class":1031},"    retriever ",[951,3980,1046],{"class":1027},[951,3982,2646],{"class":1031},[951,3984,3986,3989,3991,3993],{"class":953,"line":3985},66,[951,3987,3988],{"class":1335},"        search_type",[951,3990,1046],{"class":1027},[951,3992,2657],{"class":961},[951,3994,1330],{"class":1031},[951,3996,3998,4001,4003,4005,4007,4009,4011],{"class":953,"line":3997},67,[951,3999,4000],{"class":1335},"        search_kwargs",[951,4002,1046],{"class":1027},[951,4004,1184],{"class":1031},[951,4006,2676],{"class":961},[951,4008,2679],{"class":1031},[951,4010,2682],{"class":1145},[951,4012,4013],{"class":1031},"}\n",[951,4015,4017],{"class":953,"line":4016},68,[951,4018,3634],{"class":1031},[951,4020,4022],{"class":953,"line":4021},69,[951,4023,1055],{"emptyLinePlaceholder":1054},[951,4025,4027,4030,4032,4035,4037,4039,4041,4043,4046,4048,4050],{"class":953,"line":4026},70,[951,4028,4029],{"class":1031},"    llm ",[951,4031,1046],{"class":1027},[951,4033,4034],{"class":1031}," ChatOpenAI(",[951,4036,3796],{"class":1335},[951,4038,1046],{"class":1027},[951,4040,2488],{"class":961},[951,4042,1558],{"class":1031},[951,4044,4045],{"class":1335},"temperature",[951,4047,1046],{"class":1027},[951,4049,1190],{"class":1145},[951,4051,1124],{"class":1031},[951,4053,4055],{"class":953,"line":4054},71,[951,4056,1055],{"emptyLinePlaceholder":1054},[951,4058,4060,4063,4065],{"class":953,"line":4059},72,[951,4061,4062],{"class":1031},"    prompt ",[951,4064,1046],{"class":1027},[951,4066,4067],{"class":1031}," ChatPromptTemplate.from_template(\n",[951,4069,4071],{"class":953,"line":4070},73,[951,4072,4073],{"class":961},"        \"\"\"你是一个专业的技术文档助手。请基于以下参考信息回答用户的问题。\n",[951,4075,4077],{"class":953,"line":4076},74,[951,4078,1055],{"emptyLinePlaceholder":1054},[951,4080,4082],{"class":953,"line":4081},75,[951,4083,2564],{"class":961},[951,4085,4087],{"class":953,"line":4086},76,[951,4088,2569],{"class":961},[951,4090,4092],{"class":953,"line":4091},77,[951,4093,4094],{"class":961},"2. 如果参考信息不足以回答问题，请明确告知\n",[951,4096,4098],{"class":953,"line":4097},78,[951,4099,4100],{"class":961},"3. 回答要准确、有条理\n",[951,4102,4104],{"class":953,"line":4103},79,[951,4105,1055],{"emptyLinePlaceholder":1054},[951,4107,4109],{"class":953,"line":4108},80,[951,4110,2593],{"class":961},[951,4112,4114],{"class":953,"line":4113},81,[951,4115,2598],{"class":1145},[951,4117,4119],{"class":953,"line":4118},82,[951,4120,1055],{"emptyLinePlaceholder":1054},[951,4122,4124,4126],{"class":953,"line":4123},83,[951,4125,2607],{"class":961},[951,4127,2610],{"class":1145},[951,4129,4131],{"class":953,"line":4130},84,[951,4132,1055],{"emptyLinePlaceholder":1054},[951,4134,4136],{"class":953,"line":4135},85,[951,4137,2619],{"class":961},[951,4139,4141],{"class":953,"line":4140},86,[951,4142,3634],{"class":1031},[951,4144,4146],{"class":953,"line":4145},87,[951,4147,1055],{"emptyLinePlaceholder":1054},[951,4149,4151,4154,4156],{"class":953,"line":4150},88,[951,4152,4153],{"class":1027},"    def",[951,4155,3041],{"class":957},[951,4157,3044],{"class":1031},[951,4159,4161,4164,4166,4168,4171,4173,4175,4177,4179,4181,4183],{"class":953,"line":4160},89,[951,4162,4163],{"class":1027},"        return",[951,4165,3052],{"class":961},[951,4167,1553],{"class":1145},[951,4169,4170],{"class":961},"---",[951,4172,1553],{"class":1145},[951,4174,1204],{"class":961},[951,4176,3059],{"class":1031},[951,4178,1914],{"class":1027},[951,4180,2864],{"class":1031},[951,4182,1920],{"class":1027},[951,4184,3068],{"class":1031},[951,4186,4188],{"class":953,"line":4187},90,[951,4189,1055],{"emptyLinePlaceholder":1054},[951,4191,4193,4196,4198],{"class":953,"line":4192},91,[951,4194,4195],{"class":1031},"    chain ",[951,4197,1046],{"class":1027},[951,4199,3087],{"class":1031},[951,4201,4203,4206,4208,4210,4212,4215,4217],{"class":953,"line":4202},92,[951,4204,4205],{"class":1031},"        {",[951,4207,2537],{"class":961},[951,4209,3100],{"class":1031},[951,4211,3103],{"class":1027},[951,4213,4214],{"class":1031}," format_docs, ",[951,4216,2542],{"class":961},[951,4218,4219],{"class":1031},": RunnablePassthrough()}\n",[951,4221,4223,4226],{"class":953,"line":4222},93,[951,4224,4225],{"class":1027},"        |",[951,4227,3127],{"class":1031},[951,4229,4231,4233],{"class":953,"line":4230},94,[951,4232,4225],{"class":1027},[951,4234,3134],{"class":1031},[951,4236,4238,4240],{"class":953,"line":4237},95,[951,4239,4225],{"class":1027},[951,4241,3141],{"class":1031},[951,4243,4245],{"class":953,"line":4244},96,[951,4246,3634],{"class":1031},[951,4248,4250,4252],{"class":953,"line":4249},97,[951,4251,3049],{"class":1027},[951,4253,4254],{"class":1031}," chain\n",[951,4256,4258],{"class":953,"line":4257},98,[951,4259,1055],{"emptyLinePlaceholder":1054},[951,4261,4263],{"class":953,"line":4262},99,[951,4264,4265],{"class":997},"# ===== 主流程 =====\n",[951,4267,4269,4271,4274],{"class":953,"line":4268},100,[951,4270,3038],{"class":1027},[951,4272,4273],{"class":957}," build_knowledge_base",[951,4275,4276],{"class":1031},"():\n",[951,4278,4280],{"class":953,"line":4279},101,[951,4281,4282],{"class":961},"    \"\"\"构建知识库（首次运行）\"\"\"\n",[951,4284,4286,4288,4290,4293],{"class":953,"line":4285},102,[951,4287,1929],{"class":1145},[951,4289,1149],{"class":1031},[951,4291,4292],{"class":961},"\"正在加载文档...\"",[951,4294,1124],{"class":1031},[951,4296,4298,4301,4303,4306,4308],{"class":953,"line":4297},103,[951,4299,4300],{"class":1031},"    documents ",[951,4302,1046],{"class":1027},[951,4304,4305],{"class":1031}," load_documents(",[951,4307,3530],{"class":1145},[951,4309,1124],{"class":1031},[951,4311,4313,4315,4317,4319,4321,4323,4325,4327,4329],{"class":953,"line":4312},104,[951,4314,1929],{"class":1145},[951,4316,1149],{"class":1031},[951,4318,1152],{"class":1027},[951,4320,1155],{"class":961},[951,4322,1158],{"class":1145},[951,4324,1161],{"class":1031},[951,4326,1164],{"class":1145},[951,4328,1396],{"class":961},[951,4330,1124],{"class":1031},[951,4332,4334],{"class":953,"line":4333},105,[951,4335,1055],{"emptyLinePlaceholder":1054},[951,4337,4339,4341,4343,4346],{"class":953,"line":4338},106,[951,4340,1929],{"class":1145},[951,4342,1149],{"class":1031},[951,4344,4345],{"class":961},"\"正在分割文本...\"",[951,4347,1124],{"class":1031},[951,4349,4351,4354,4356],{"class":953,"line":4350},107,[951,4352,4353],{"class":1031},"    chunks ",[951,4355,1046],{"class":1027},[951,4357,4358],{"class":1031}," split_documents(documents)\n",[951,4360,4362,4364,4366,4368,4371,4373,4375,4377,4380],{"class":953,"line":4361},108,[951,4363,1929],{"class":1145},[951,4365,1149],{"class":1031},[951,4367,1152],{"class":1027},[951,4369,4370],{"class":961},"\"分割为 ",[951,4372,1158],{"class":1145},[951,4374,1635],{"class":1031},[951,4376,1164],{"class":1145},[951,4378,4379],{"class":961}," 个文本块\"",[951,4381,1124],{"class":1031},[951,4383,4385],{"class":953,"line":4384},109,[951,4386,1055],{"emptyLinePlaceholder":1054},[951,4388,4390,4392,4394,4397],{"class":953,"line":4389},110,[951,4391,1929],{"class":1145},[951,4393,1149],{"class":1031},[951,4395,4396],{"class":961},"\"正在创建向量存储...\"",[951,4398,1124],{"class":1031},[951,4400,4402,4404,4406,4409,4411],{"class":953,"line":4401},111,[951,4403,3807],{"class":1031},[951,4405,1046],{"class":1027},[951,4407,4408],{"class":1031}," create_vectorstore(chunks, ",[951,4410,3541],{"class":1145},[951,4412,1124],{"class":1031},[951,4414,4416,4418,4420,4422,4425,4427,4429,4431,4434],{"class":953,"line":4415},112,[951,4417,1929],{"class":1145},[951,4419,1149],{"class":1031},[951,4421,1152],{"class":1027},[951,4423,4424],{"class":961},"\"向量存储创建完成，共 ",[951,4426,1184],{"class":1145},[951,4428,2111],{"class":1031},[951,4430,1164],{"class":1145},[951,4432,4433],{"class":961}," 条记录\"",[951,4435,1124],{"class":1031},[951,4437,4439,4441],{"class":953,"line":4438},113,[951,4440,3049],{"class":1027},[951,4442,3861],{"class":1031},[951,4444,4446],{"class":953,"line":4445},114,[951,4447,1055],{"emptyLinePlaceholder":1054},[951,4449,4451,4453,4456],{"class":953,"line":4450},115,[951,4452,3038],{"class":1027},[951,4454,4455],{"class":957}," chat",[951,4457,4276],{"class":1031},[951,4459,4461],{"class":953,"line":4460},116,[951,4462,4463],{"class":961},"    \"\"\"交互式问答\"\"\"\n",[951,4465,4467,4469,4471,4474],{"class":953,"line":4466},117,[951,4468,1929],{"class":1145},[951,4470,1149],{"class":1031},[951,4472,4473],{"class":961},"\"正在加载知识库...\"",[951,4475,1124],{"class":1031},[951,4477,4479,4481,4483,4486,4488],{"class":953,"line":4478},118,[951,4480,3807],{"class":1031},[951,4482,1046],{"class":1027},[951,4484,4485],{"class":1031}," load_vectorstore(",[951,4487,3541],{"class":1145},[951,4489,1124],{"class":1031},[951,4491,4493,4495,4497],{"class":953,"line":4492},119,[951,4494,4195],{"class":1031},[951,4496,1046],{"class":1027},[951,4498,4499],{"class":1031}," create_rag_chain(vectorstore)\n",[951,4501,4503],{"class":953,"line":4502},120,[951,4504,1055],{"emptyLinePlaceholder":1054},[951,4506,4508,4510,4512,4515,4517,4519],{"class":953,"line":4507},121,[951,4509,1929],{"class":1145},[951,4511,1149],{"class":1031},[951,4513,4514],{"class":961},"\"知识库已就绪！输入问题开始对话（输入 'quit' 退出）",[951,4516,1563],{"class":1145},[951,4518,1204],{"class":961},[951,4520,1124],{"class":1031},[951,4522,4524,4527,4530],{"class":953,"line":4523},122,[951,4525,4526],{"class":1027},"    while",[951,4528,4529],{"class":1145}," True",[951,4531,4532],{"class":1031},":\n",[951,4534,4536,4539,4541,4544,4546,4549],{"class":953,"line":4535},123,[951,4537,4538],{"class":1031},"        question ",[951,4540,1046],{"class":1027},[951,4542,4543],{"class":1145}," input",[951,4545,1149],{"class":1031},[951,4547,4548],{"class":961},"\"你: \"",[951,4550,4551],{"class":1031},").strip()\n",[951,4553,4555,4558,4561,4563,4566,4569,4571,4574,4576,4579],{"class":953,"line":4554},124,[951,4556,4557],{"class":1027},"        if",[951,4559,4560],{"class":1031}," question.lower() ",[951,4562,1920],{"class":1027},[951,4564,4565],{"class":1031}," (",[951,4567,4568],{"class":961},"\"quit\"",[951,4570,1558],{"class":1031},[951,4572,4573],{"class":961},"\"exit\"",[951,4575,1558],{"class":1031},[951,4577,4578],{"class":961},"\"q\"",[951,4580,3581],{"class":1031},[951,4582,4584],{"class":953,"line":4583},125,[951,4585,4586],{"class":1027},"            break\n",[951,4588,4590,4592,4595],{"class":953,"line":4589},126,[951,4591,4557],{"class":1027},[951,4593,4594],{"class":1027}," not",[951,4596,4597],{"class":1031}," question:\n",[951,4599,4601],{"class":953,"line":4600},127,[951,4602,4603],{"class":1027},"            continue\n",[951,4605,4607],{"class":953,"line":4606},128,[951,4608,1055],{"emptyLinePlaceholder":1054},[951,4610,4612,4615,4617],{"class":953,"line":4611},129,[951,4613,4614],{"class":1031},"        answer ",[951,4616,1046],{"class":1027},[951,4618,4619],{"class":1031}," chain.invoke(question)\n",[951,4621,4623,4626,4628,4630,4632,4634,4637,4639,4642,4644,4646],{"class":953,"line":4622},130,[951,4624,4625],{"class":1145},"        print",[951,4627,1149],{"class":1031},[951,4629,1152],{"class":1027},[951,4631,1204],{"class":961},[951,4633,1563],{"class":1145},[951,4635,4636],{"class":961},"助手: ",[951,4638,1184],{"class":1145},[951,4640,4641],{"class":1031},"answer",[951,4643,2298],{"class":1145},[951,4645,1204],{"class":961},[951,4647,1124],{"class":1031},[951,4649,4651],{"class":953,"line":4650},131,[951,4652,1055],{"emptyLinePlaceholder":1054},[951,4654,4656,4659,4662,4665,4668],{"class":953,"line":4655},132,[951,4657,4658],{"class":1027},"if",[951,4660,4661],{"class":1145}," __name__",[951,4663,4664],{"class":1027}," ==",[951,4666,4667],{"class":961}," \"__main__\"",[951,4669,4532],{"class":1031},[951,4671,4673,4676],{"class":953,"line":4672},133,[951,4674,4675],{"class":1027},"    import",[951,4677,4678],{"class":1031}," sys\n",[951,4680,4682,4685,4688,4691,4694,4697,4700,4703,4706,4708,4711,4714],{"class":953,"line":4681},134,[951,4683,4684],{"class":1027},"    if",[951,4686,4687],{"class":1145}," len",[951,4689,4690],{"class":1031},"(sys.argv) ",[951,4692,4693],{"class":1027},">",[951,4695,4696],{"class":1145}," 1",[951,4698,4699],{"class":1027}," and",[951,4701,4702],{"class":1031}," sys.argv[",[951,4704,4705],{"class":1145},"1",[951,4707,1043],{"class":1031},[951,4709,4710],{"class":1027},"==",[951,4712,4713],{"class":961}," \"build\"",[951,4715,4532],{"class":1031},[951,4717,4719],{"class":953,"line":4718},135,[951,4720,4721],{"class":1031},"        build_knowledge_base()\n",[951,4723,4725,4728],{"class":953,"line":4724},136,[951,4726,4727],{"class":1027},"    else",[951,4729,4532],{"class":1031},[951,4731,4733],{"class":953,"line":4732},137,[951,4734,4735],{"class":1031},"        chat()\n",[896,4737,4738],{},"使用方式：",[926,4740,4742],{"className":945,"code":4741,"language":947,"meta":11,"style":11},"# 首次运行：构建知识库\npython rag_app.py build\n\n# 后续运行：交互式问答\npython rag_app.py\n",[934,4743,4744,4749,4759,4763,4768],{"__ignoreMap":11},[951,4745,4746],{"class":953,"line":954},[951,4747,4748],{"class":997},"# 首次运行：构建知识库\n",[951,4750,4751,4753,4756],{"class":953,"line":977},[951,4752,1020],{"class":957},[951,4754,4755],{"class":961}," rag_app.py",[951,4757,4758],{"class":961}," build\n",[951,4760,4761],{"class":953,"line":987},[951,4762,1055],{"emptyLinePlaceholder":1054},[951,4764,4765],{"class":953,"line":1001},[951,4766,4767],{"class":997},"# 后续运行：交互式问答\n",[951,4769,4770,4772],{"class":953,"line":1063},[951,4771,1020],{"class":957},[951,4773,4774],{"class":961}," rag_app.py\n",[892,4776,4777],{"id":4777},"优化技巧",[896,4779,4780],{},"基础 RAG 系统搭建完成后，可以通过以下技术进一步提升效果。",[940,4782,4784],{"id":4783},"重排序reranking","重排序（Reranking）",[896,4786,4787],{},"初步检索返回的 Top-K 结果中，排序可能不够精确。使用交叉编码器（Cross-Encoder）对结果进行重排序，可以显著提高相关性。",[926,4789,4791],{"className":1018,"code":4790,"language":1020,"meta":11,"style":11},"from langchain.retrievers import ContextualCompressionRetriever\nfrom langchain.retrievers.document_compressors import CrossEncoderReranker\nfrom langchain_community.cross_encoders import HuggingFaceCrossEncoder\n\n# 加载重排序模型\ncross_encoder = HuggingFaceCrossEncoder(model_name=\"BAAI\u002Fbge-reranker-base\")\ncompressor = CrossEncoderReranker(model=cross_encoder, top_n=3)\n\n# 创建带重排序的检索器\nreranking_retriever = ContextualCompressionRetriever(\n    base_compressor=compressor,\n    base_retriever=vectorstore.as_retriever(search_kwargs={\"k\": 10})\n)\n\n# 先检索 10 条，再用 reranker 精排出 top 3\nresults = reranking_retriever.invoke(\"如何优化 Docker 镜像大小？\")\n",[934,4792,4793,4805,4817,4829,4833,4838,4858,4884,4888,4893,4903,4913,4939,4943,4947,4952],{"__ignoreMap":11},[951,4794,4795,4797,4800,4802],{"class":953,"line":954},[951,4796,1091],{"class":1027},[951,4798,4799],{"class":1031}," langchain.retrievers ",[951,4801,1028],{"class":1027},[951,4803,4804],{"class":1031}," ContextualCompressionRetriever\n",[951,4806,4807,4809,4812,4814],{"class":953,"line":977},[951,4808,1091],{"class":1027},[951,4810,4811],{"class":1031}," langchain.retrievers.document_compressors ",[951,4813,1028],{"class":1027},[951,4815,4816],{"class":1031}," CrossEncoderReranker\n",[951,4818,4819,4821,4824,4826],{"class":953,"line":987},[951,4820,1091],{"class":1027},[951,4822,4823],{"class":1031}," langchain_community.cross_encoders ",[951,4825,1028],{"class":1027},[951,4827,4828],{"class":1031}," HuggingFaceCrossEncoder\n",[951,4830,4831],{"class":953,"line":1001},[951,4832,1055],{"emptyLinePlaceholder":1054},[951,4834,4835],{"class":953,"line":1063},[951,4836,4837],{"class":997},"# 加载重排序模型\n",[951,4839,4840,4843,4845,4848,4851,4853,4856],{"class":953,"line":1137},[951,4841,4842],{"class":1031},"cross_encoder ",[951,4844,1046],{"class":1027},[951,4846,4847],{"class":1031}," HuggingFaceCrossEncoder(",[951,4849,4850],{"class":1335},"model_name",[951,4852,1046],{"class":1027},[951,4854,4855],{"class":961},"\"BAAI\u002Fbge-reranker-base\"",[951,4857,1124],{"class":1031},[951,4859,4860,4863,4865,4868,4870,4872,4875,4878,4880,4882],{"class":953,"line":1142},[951,4861,4862],{"class":1031},"compressor ",[951,4864,1046],{"class":1027},[951,4866,4867],{"class":1031}," CrossEncoderReranker(",[951,4869,3796],{"class":1335},[951,4871,1046],{"class":1027},[951,4873,4874],{"class":1031},"cross_encoder, ",[951,4876,4877],{"class":1335},"top_n",[951,4879,1046],{"class":1027},[951,4881,2273],{"class":1145},[951,4883,1124],{"class":1031},[951,4885,4886],{"class":953,"line":1172},[951,4887,1055],{"emptyLinePlaceholder":1054},[951,4889,4890],{"class":953,"line":1209},[951,4891,4892],{"class":997},"# 创建带重排序的检索器\n",[951,4894,4895,4898,4900],{"class":953,"line":1236},[951,4896,4897],{"class":1031},"reranking_retriever ",[951,4899,1046],{"class":1027},[951,4901,4902],{"class":1031}," ContextualCompressionRetriever(\n",[951,4904,4905,4908,4910],{"class":953,"line":1378},[951,4906,4907],{"class":1335},"    base_compressor",[951,4909,1046],{"class":1027},[951,4911,4912],{"class":1031},"compressor,\n",[951,4914,4915,4918,4920,4923,4926,4928,4930,4932,4934,4937],{"class":953,"line":1645},[951,4916,4917],{"class":1335},"    base_retriever",[951,4919,1046],{"class":1027},[951,4921,4922],{"class":1031},"vectorstore.as_retriever(",[951,4924,4925],{"class":1335},"search_kwargs",[951,4927,1046],{"class":1027},[951,4929,1184],{"class":1031},[951,4931,2676],{"class":961},[951,4933,2679],{"class":1031},[951,4935,4936],{"class":1145},"10",[951,4938,2821],{"class":1031},[951,4940,4941],{"class":953,"line":1674},[951,4942,1124],{"class":1031},[951,4944,4945],{"class":953,"line":1837},[951,4946,1055],{"emptyLinePlaceholder":1054},[951,4948,4949],{"class":953,"line":1843},[951,4950,4951],{"class":997},"# 先检索 10 条，再用 reranker 精排出 top 3\n",[951,4953,4954,4956,4958,4961,4964],{"class":953,"line":1849},[951,4955,2260],{"class":1031},[951,4957,1046],{"class":1027},[951,4959,4960],{"class":1031}," reranking_retriever.invoke(",[951,4962,4963],{"class":961},"\"如何优化 Docker 镜像大小？\"",[951,4965,1124],{"class":1031},[1445,4967,4968],{},"\n重排序是提升 RAG 检索质量最有效的方法之一。建议在基础检索阶段多召回一些结果（如 k=10~20），然后通过 Reranker 精选出最相关的 3~5 条。\n",[940,4970,4972],{"id":4971},"混合检索hybrid-search","混合检索（Hybrid Search）",[896,4974,4975],{},"结合向量语义检索和关键词检索（BM25）的优势，提高检索的覆盖面和准确性。",[926,4977,4979],{"className":1018,"code":4978,"language":1020,"meta":11,"style":11},"from langchain.retrievers import EnsembleRetriever\nfrom langchain_community.retrievers import BM25Retriever\n\n# BM25 关键词检索器\nbm25_retriever = BM25Retriever.from_documents(chunks)\nbm25_retriever.k = 5\n\n# 向量检索器\nvector_retriever = vectorstore.as_retriever(search_kwargs={\"k\": 5})\n\n# 混合检索（各占 50% 权重）\nhybrid_retriever = EnsembleRetriever(\n    retrievers=[bm25_retriever, vector_retriever],\n    weights=[0.4, 0.6]  # BM25 权重 40%，向量检索权重 60%\n)\n\nresults = hybrid_retriever.invoke(\"Redis 分布式锁的实现原理\")\n",[934,4980,4981,4992,5004,5008,5013,5023,5033,5037,5042,5067,5071,5076,5086,5096,5118,5122,5126],{"__ignoreMap":11},[951,4982,4983,4985,4987,4989],{"class":953,"line":954},[951,4984,1091],{"class":1027},[951,4986,4799],{"class":1031},[951,4988,1028],{"class":1027},[951,4990,4991],{"class":1031}," EnsembleRetriever\n",[951,4993,4994,4996,4999,5001],{"class":953,"line":977},[951,4995,1091],{"class":1027},[951,4997,4998],{"class":1031}," langchain_community.retrievers ",[951,5000,1028],{"class":1027},[951,5002,5003],{"class":1031}," BM25Retriever\n",[951,5005,5006],{"class":953,"line":987},[951,5007,1055],{"emptyLinePlaceholder":1054},[951,5009,5010],{"class":953,"line":1001},[951,5011,5012],{"class":997},"# BM25 关键词检索器\n",[951,5014,5015,5018,5020],{"class":953,"line":1063},[951,5016,5017],{"class":1031},"bm25_retriever ",[951,5019,1046],{"class":1027},[951,5021,5022],{"class":1031}," BM25Retriever.from_documents(chunks)\n",[951,5024,5025,5028,5030],{"class":953,"line":1137},[951,5026,5027],{"class":1031},"bm25_retriever.k ",[951,5029,1046],{"class":1027},[951,5031,5032],{"class":1145}," 5\n",[951,5034,5035],{"class":953,"line":1142},[951,5036,1055],{"emptyLinePlaceholder":1054},[951,5038,5039],{"class":953,"line":1172},[951,5040,5041],{"class":997},"# 向量检索器\n",[951,5043,5044,5047,5049,5052,5054,5056,5058,5060,5062,5065],{"class":953,"line":1209},[951,5045,5046],{"class":1031},"vector_retriever ",[951,5048,1046],{"class":1027},[951,5050,5051],{"class":1031}," vectorstore.as_retriever(",[951,5053,4925],{"class":1335},[951,5055,1046],{"class":1027},[951,5057,1184],{"class":1031},[951,5059,2676],{"class":961},[951,5061,2679],{"class":1031},[951,5063,5064],{"class":1145},"5",[951,5066,2821],{"class":1031},[951,5068,5069],{"class":953,"line":1236},[951,5070,1055],{"emptyLinePlaceholder":1054},[951,5072,5073],{"class":953,"line":1378},[951,5074,5075],{"class":997},"# 混合检索（各占 50% 权重）\n",[951,5077,5078,5081,5083],{"class":953,"line":1645},[951,5079,5080],{"class":1031},"hybrid_retriever ",[951,5082,1046],{"class":1027},[951,5084,5085],{"class":1031}," EnsembleRetriever(\n",[951,5087,5088,5091,5093],{"class":953,"line":1674},[951,5089,5090],{"class":1335},"    retrievers",[951,5092,1046],{"class":1027},[951,5094,5095],{"class":1031},"[bm25_retriever, vector_retriever],\n",[951,5097,5098,5101,5103,5105,5108,5110,5113,5115],{"class":953,"line":1837},[951,5099,5100],{"class":1335},"    weights",[951,5102,1046],{"class":1027},[951,5104,1548],{"class":1031},[951,5106,5107],{"class":1145},"0.4",[951,5109,1558],{"class":1031},[951,5111,5112],{"class":1145},"0.6",[951,5114,1598],{"class":1031},[951,5116,5117],{"class":997},"# BM25 权重 40%，向量检索权重 60%\n",[951,5119,5120],{"class":953,"line":1843},[951,5121,1124],{"class":1031},[951,5123,5124],{"class":953,"line":1849},[951,5125,1055],{"emptyLinePlaceholder":1054},[951,5127,5128,5130,5132,5135,5138],{"class":953,"line":1854},[951,5129,2260],{"class":1031},[951,5131,1046],{"class":1027},[951,5133,5134],{"class":1031}," hybrid_retriever.invoke(",[951,5136,5137],{"class":961},"\"Redis 分布式锁的实现原理\"",[951,5139,1124],{"class":1031},[940,5141,5143],{"id":5142},"查询转换query-transformation","查询转换（Query Transformation）",[896,5145,5146],{},"用户的原始查询可能表述不清或过于简略，通过 LLM 对查询进行改写，可以提高检索效果。",[926,5148,5150],{"className":1018,"code":5149,"language":1020,"meta":11,"style":11},"from langchain_core.prompts import ChatPromptTemplate\nfrom langchain_openai import ChatOpenAI\n\n# 查询改写\nrewrite_prompt = ChatPromptTemplate.from_template(\n    \"\"\"你是一个查询优化助手。请将用户的问题改写为更适合搜索的形式。\n要求：保持原意，扩展关键词，消除歧义。\n\n原始问题：{question}\n\n改写后的问题：\"\"\"\n)\n\nquery_rewriter = rewrite_prompt | ChatOpenAI(model=\"gpt-4o-mini\") | StrOutputParser()\n\n# 多查询生成：从不同角度生成多个查询\nmulti_query_prompt = ChatPromptTemplate.from_template(\n    \"\"\"请从不同角度为以下问题生成 3 个替代查询，每行一个。\n\n原始问题：{question}\n\n替代查询：\"\"\"\n)\n",[934,5151,5152,5162,5172,5176,5181,5190,5195,5200,5204,5211,5215,5220,5224,5228,5255,5259,5264,5273,5278,5282,5288,5292,5297],{"__ignoreMap":11},[951,5153,5154,5156,5158,5160],{"class":953,"line":954},[951,5155,1091],{"class":1027},[951,5157,2934],{"class":1031},[951,5159,1028],{"class":1027},[951,5161,2939],{"class":1031},[951,5163,5164,5166,5168,5170],{"class":953,"line":977},[951,5165,1091],{"class":1027},[951,5167,1981],{"class":1031},[951,5169,1028],{"class":1027},[951,5171,2436],{"class":1031},[951,5173,5174],{"class":953,"line":987},[951,5175,1055],{"emptyLinePlaceholder":1054},[951,5177,5178],{"class":953,"line":1001},[951,5179,5180],{"class":997},"# 查询改写\n",[951,5182,5183,5186,5188],{"class":953,"line":1063},[951,5184,5185],{"class":1031},"rewrite_prompt ",[951,5187,1046],{"class":1027},[951,5189,4067],{"class":1031},[951,5191,5192],{"class":953,"line":1137},[951,5193,5194],{"class":961},"    \"\"\"你是一个查询优化助手。请将用户的问题改写为更适合搜索的形式。\n",[951,5196,5197],{"class":953,"line":1142},[951,5198,5199],{"class":961},"要求：保持原意，扩展关键词，消除歧义。\n",[951,5201,5202],{"class":953,"line":1172},[951,5203,1055],{"emptyLinePlaceholder":1054},[951,5205,5206,5209],{"class":953,"line":1209},[951,5207,5208],{"class":961},"原始问题：",[951,5210,2610],{"class":1145},[951,5212,5213],{"class":953,"line":1236},[951,5214,1055],{"emptyLinePlaceholder":1054},[951,5216,5217],{"class":953,"line":1378},[951,5218,5219],{"class":961},"改写后的问题：\"\"\"\n",[951,5221,5222],{"class":953,"line":1645},[951,5223,1124],{"class":1031},[951,5225,5226],{"class":953,"line":1674},[951,5227,1055],{"emptyLinePlaceholder":1054},[951,5229,5230,5233,5235,5238,5240,5242,5244,5246,5248,5251,5253],{"class":953,"line":1837},[951,5231,5232],{"class":1031},"query_rewriter ",[951,5234,1046],{"class":1027},[951,5236,5237],{"class":1031}," rewrite_prompt ",[951,5239,3103],{"class":1027},[951,5241,4034],{"class":1031},[951,5243,3796],{"class":1335},[951,5245,1046],{"class":1027},[951,5247,2488],{"class":961},[951,5249,5250],{"class":1031},") ",[951,5252,3103],{"class":1027},[951,5254,3141],{"class":1031},[951,5256,5257],{"class":953,"line":1843},[951,5258,1055],{"emptyLinePlaceholder":1054},[951,5260,5261],{"class":953,"line":1849},[951,5262,5263],{"class":997},"# 多查询生成：从不同角度生成多个查询\n",[951,5265,5266,5269,5271],{"class":953,"line":1854},[951,5267,5268],{"class":1031},"multi_query_prompt ",[951,5270,1046],{"class":1027},[951,5272,4067],{"class":1031},[951,5274,5275],{"class":953,"line":1860},[951,5276,5277],{"class":961},"    \"\"\"请从不同角度为以下问题生成 3 个替代查询，每行一个。\n",[951,5279,5280],{"class":953,"line":1866},[951,5281,1055],{"emptyLinePlaceholder":1054},[951,5283,5284,5286],{"class":953,"line":1872},[951,5285,5208],{"class":961},[951,5287,2610],{"class":1145},[951,5289,5290],{"class":953,"line":1877},[951,5291,1055],{"emptyLinePlaceholder":1054},[951,5293,5294],{"class":953,"line":1883},[951,5295,5296],{"class":961},"替代查询：\"\"\"\n",[951,5298,5299],{"class":953,"line":1889},[951,5300,1124],{"class":1031},[940,5302,5304],{"id":5303},"父文档检索parent-document-retriever","父文档检索（Parent Document Retriever）",[896,5306,5307],{},"用小块进行检索（提高匹配精度），但返回其所在的大块（提供更完整的上下文）。",[926,5309,5311],{"className":1018,"code":5310,"language":1020,"meta":11,"style":11},"from langchain.retrievers import ParentDocumentRetriever\nfrom langchain.storage import InMemoryStore\n\n# 小块用于检索，大块用于返回\nchild_splitter = RecursiveCharacterTextSplitter(chunk_size=200)\nparent_splitter = RecursiveCharacterTextSplitter(chunk_size=1000)\n\nstore = InMemoryStore()\n\nparent_retriever = ParentDocumentRetriever(\n    vectorstore=vectorstore,\n    docstore=store,\n    child_splitter=child_splitter,\n    parent_splitter=parent_splitter,\n)\n\n# 添加文档\nparent_retriever.add_documents(documents)\n\n# 检索时返回的是包含匹配小块的完整大块\nresults = parent_retriever.invoke(\"什么是容器编排？\")\n",[934,5312,5313,5324,5336,5340,5345,5364,5382,5386,5396,5400,5410,5420,5430,5440,5450,5454,5458,5463,5468,5472,5477],{"__ignoreMap":11},[951,5314,5315,5317,5319,5321],{"class":953,"line":954},[951,5316,1091],{"class":1027},[951,5318,4799],{"class":1031},[951,5320,1028],{"class":1027},[951,5322,5323],{"class":1031}," ParentDocumentRetriever\n",[951,5325,5326,5328,5331,5333],{"class":953,"line":977},[951,5327,1091],{"class":1027},[951,5329,5330],{"class":1031}," langchain.storage ",[951,5332,1028],{"class":1027},[951,5334,5335],{"class":1031}," InMemoryStore\n",[951,5337,5338],{"class":953,"line":987},[951,5339,1055],{"emptyLinePlaceholder":1054},[951,5341,5342],{"class":953,"line":1001},[951,5343,5344],{"class":997},"# 小块用于检索，大块用于返回\n",[951,5346,5347,5350,5352,5355,5358,5360,5362],{"class":953,"line":1063},[951,5348,5349],{"class":1031},"child_splitter ",[951,5351,1046],{"class":1027},[951,5353,5354],{"class":1031}," RecursiveCharacterTextSplitter(",[951,5356,5357],{"class":1335},"chunk_size",[951,5359,1046],{"class":1027},[951,5361,1196],{"class":1145},[951,5363,1124],{"class":1031},[951,5365,5366,5369,5371,5373,5375,5377,5380],{"class":953,"line":1137},[951,5367,5368],{"class":1031},"parent_splitter ",[951,5370,1046],{"class":1027},[951,5372,5354],{"class":1031},[951,5374,5357],{"class":1335},[951,5376,1046],{"class":1027},[951,5378,5379],{"class":1145},"1000",[951,5381,1124],{"class":1031},[951,5383,5384],{"class":953,"line":1142},[951,5385,1055],{"emptyLinePlaceholder":1054},[951,5387,5388,5391,5393],{"class":953,"line":1172},[951,5389,5390],{"class":1031},"store ",[951,5392,1046],{"class":1027},[951,5394,5395],{"class":1031}," InMemoryStore()\n",[951,5397,5398],{"class":953,"line":1209},[951,5399,1055],{"emptyLinePlaceholder":1054},[951,5401,5402,5405,5407],{"class":953,"line":1236},[951,5403,5404],{"class":1031},"parent_retriever ",[951,5406,1046],{"class":1027},[951,5408,5409],{"class":1031}," ParentDocumentRetriever(\n",[951,5411,5412,5415,5417],{"class":953,"line":1378},[951,5413,5414],{"class":1335},"    vectorstore",[951,5416,1046],{"class":1027},[951,5418,5419],{"class":1031},"vectorstore,\n",[951,5421,5422,5425,5427],{"class":953,"line":1645},[951,5423,5424],{"class":1335},"    docstore",[951,5426,1046],{"class":1027},[951,5428,5429],{"class":1031},"store,\n",[951,5431,5432,5435,5437],{"class":953,"line":1674},[951,5433,5434],{"class":1335},"    child_splitter",[951,5436,1046],{"class":1027},[951,5438,5439],{"class":1031},"child_splitter,\n",[951,5441,5442,5445,5447],{"class":953,"line":1837},[951,5443,5444],{"class":1335},"    parent_splitter",[951,5446,1046],{"class":1027},[951,5448,5449],{"class":1031},"parent_splitter,\n",[951,5451,5452],{"class":953,"line":1843},[951,5453,1124],{"class":1031},[951,5455,5456],{"class":953,"line":1849},[951,5457,1055],{"emptyLinePlaceholder":1054},[951,5459,5460],{"class":953,"line":1854},[951,5461,5462],{"class":997},"# 添加文档\n",[951,5464,5465],{"class":953,"line":1860},[951,5466,5467],{"class":1031},"parent_retriever.add_documents(documents)\n",[951,5469,5470],{"class":953,"line":1866},[951,5471,1055],{"emptyLinePlaceholder":1054},[951,5473,5474],{"class":953,"line":1872},[951,5475,5476],{"class":997},"# 检索时返回的是包含匹配小块的完整大块\n",[951,5478,5479,5481,5483,5486,5489],{"class":953,"line":1877},[951,5480,2260],{"class":1031},[951,5482,1046],{"class":1027},[951,5484,5485],{"class":1031}," parent_retriever.invoke(",[951,5487,5488],{"class":961},"\"什么是容器编排？\"",[951,5490,1124],{"class":1031},[892,5492,5493],{"id":5493},"评估方法",[896,5495,5496],{},"RAG 系统的效果评估是保证质量的关键环节。",[940,5498,5499],{"id":5499},"评估维度",[5501,5502,5503,5518],"table",{},[5504,5505,5506],"thead",{},[5507,5508,5509,5512,5515],"tr",{},[5510,5511,5499],"th",{},[5510,5513,5514],{},"说明",[5510,5516,5517],{},"衡量指标",[5519,5520,5521,5535,5548],"tbody",{},[5507,5522,5523,5529,5532],{},[5524,5525,5526],"td",{},[906,5527,5528],{},"检索质量",[5524,5530,5531],{},"检索到的文档是否与问题相关",[5524,5533,5534],{},"召回率、精确率、MRR",[5507,5536,5537,5542,5545],{},[5524,5538,5539],{},[906,5540,5541],{},"生成质量",[5524,5543,5544],{},"生成的回答是否准确、完整",[5524,5546,5547],{},"忠实度、相关性、完整性",[5507,5549,5550,5555,5558],{},[5524,5551,5552],{},[906,5553,5554],{},"端到端效果",[5524,5556,5557],{},"最终回答是否满足用户需求",[5524,5559,5560],{},"用户满意度、正确率",[940,5562,5564],{"id":5563},"使用-ragas-评估","使用 RAGAS 评估",[896,5566,5567],{},"RAGAS 是专门用于 RAG 系统评估的开源框架：",[926,5569,5571],{"className":1018,"code":5570,"language":1020,"meta":11,"style":11},"# pip install ragas\nfrom ragas import evaluate\nfrom ragas.metrics import (\n    faithfulness,        # 忠实度：回答是否忠于检索到的文档\n    answer_relevancy,    # 相关性：回答是否与问题相关\n    context_precision,   # 上下文精确率：检索结果是否精准\n    context_recall,      # 上下文召回率：是否检索到了所有相关信息\n)\nfrom datasets import Dataset\n\n# 准备评估数据\neval_data = {\n    \"question\": [\"什么是 Kubernetes？\", \"Docker 和虚拟机的区别？\"],\n    \"answer\": [\"Kubernetes 是一个容器编排平台...\", \"Docker 使用容器技术...\"],\n    \"contexts\": [\n        [\"Kubernetes 是一个开源的容器编排系统...\"],\n        [\"Docker 是一个容器化平台，与传统虚拟机不同...\"],\n    ],\n    \"ground_truth\": [\n        \"Kubernetes 是一个开源的容器编排平台，用于自动化部署和管理容器化应用\",\n        \"Docker 使用容器技术共享宿主机内核，而虚拟机使用 Hypervisor 模拟完整操作系统\",\n    ],\n}\n\ndataset = Dataset.from_dict(eval_data)\n\n# 运行评估\nresult = evaluate(\n    dataset,\n    metrics=[faithfulness, answer_relevancy, context_precision, context_recall],\n)\nprint(result)\n",[934,5572,5573,5578,5590,5601,5609,5617,5625,5633,5637,5649,5653,5658,5668,5686,5703,5711,5721,5730,5735,5742,5749,5756,5760,5764,5768,5778,5782,5787,5797,5802,5812,5816],{"__ignoreMap":11},[951,5574,5575],{"class":953,"line":954},[951,5576,5577],{"class":997},"# pip install ragas\n",[951,5579,5580,5582,5585,5587],{"class":953,"line":977},[951,5581,1091],{"class":1027},[951,5583,5584],{"class":1031}," ragas ",[951,5586,1028],{"class":1027},[951,5588,5589],{"class":1031}," evaluate\n",[951,5591,5592,5594,5597,5599],{"class":953,"line":987},[951,5593,1091],{"class":1027},[951,5595,5596],{"class":1031}," ragas.metrics ",[951,5598,1028],{"class":1027},[951,5600,3087],{"class":1031},[951,5602,5603,5606],{"class":953,"line":1001},[951,5604,5605],{"class":1031},"    faithfulness,        ",[951,5607,5608],{"class":997},"# 忠实度：回答是否忠于检索到的文档\n",[951,5610,5611,5614],{"class":953,"line":1063},[951,5612,5613],{"class":1031},"    answer_relevancy,    ",[951,5615,5616],{"class":997},"# 相关性：回答是否与问题相关\n",[951,5618,5619,5622],{"class":953,"line":1137},[951,5620,5621],{"class":1031},"    context_precision,   ",[951,5623,5624],{"class":997},"# 上下文精确率：检索结果是否精准\n",[951,5626,5627,5630],{"class":953,"line":1142},[951,5628,5629],{"class":1031},"    context_recall,      ",[951,5631,5632],{"class":997},"# 上下文召回率：是否检索到了所有相关信息\n",[951,5634,5635],{"class":953,"line":1172},[951,5636,1124],{"class":1031},[951,5638,5639,5641,5644,5646],{"class":953,"line":1209},[951,5640,1091],{"class":1027},[951,5642,5643],{"class":1031}," datasets ",[951,5645,1028],{"class":1027},[951,5647,5648],{"class":1031}," Dataset\n",[951,5650,5651],{"class":953,"line":1236},[951,5652,1055],{"emptyLinePlaceholder":1054},[951,5654,5655],{"class":953,"line":1378},[951,5656,5657],{"class":997},"# 准备评估数据\n",[951,5659,5660,5663,5665],{"class":953,"line":1645},[951,5661,5662],{"class":1031},"eval_data ",[951,5664,1046],{"class":1027},[951,5666,5667],{"class":1031}," {\n",[951,5669,5670,5673,5676,5679,5681,5684],{"class":953,"line":1674},[951,5671,5672],{"class":961},"    \"question\"",[951,5674,5675],{"class":1031},": [",[951,5677,5678],{"class":961},"\"什么是 Kubernetes？\"",[951,5680,1558],{"class":1031},[951,5682,5683],{"class":961},"\"Docker 和虚拟机的区别？\"",[951,5685,2545],{"class":1031},[951,5687,5688,5691,5693,5696,5698,5701],{"class":953,"line":1837},[951,5689,5690],{"class":961},"    \"answer\"",[951,5692,5675],{"class":1031},[951,5694,5695],{"class":961},"\"Kubernetes 是一个容器编排平台...\"",[951,5697,1558],{"class":1031},[951,5699,5700],{"class":961},"\"Docker 使用容器技术...\"",[951,5702,2545],{"class":1031},[951,5704,5705,5708],{"class":953,"line":1843},[951,5706,5707],{"class":961},"    \"contexts\"",[951,5709,5710],{"class":1031},": [\n",[951,5712,5713,5716,5719],{"class":953,"line":1849},[951,5714,5715],{"class":1031},"        [",[951,5717,5718],{"class":961},"\"Kubernetes 是一个开源的容器编排系统...\"",[951,5720,2545],{"class":1031},[951,5722,5723,5725,5728],{"class":953,"line":1854},[951,5724,5715],{"class":1031},[951,5726,5727],{"class":961},"\"Docker 是一个容器化平台，与传统虚拟机不同...\"",[951,5729,2545],{"class":1031},[951,5731,5732],{"class":953,"line":1860},[951,5733,5734],{"class":1031},"    ],\n",[951,5736,5737,5740],{"class":953,"line":1866},[951,5738,5739],{"class":961},"    \"ground_truth\"",[951,5741,5710],{"class":1031},[951,5743,5744,5747],{"class":953,"line":1872},[951,5745,5746],{"class":961},"        \"Kubernetes 是一个开源的容器编排平台，用于自动化部署和管理容器化应用\"",[951,5748,1330],{"class":1031},[951,5750,5751,5754],{"class":953,"line":1877},[951,5752,5753],{"class":961},"        \"Docker 使用容器技术共享宿主机内核，而虚拟机使用 Hypervisor 模拟完整操作系统\"",[951,5755,1330],{"class":1031},[951,5757,5758],{"class":953,"line":1883},[951,5759,5734],{"class":1031},[951,5761,5762],{"class":953,"line":1889},[951,5763,4013],{"class":1031},[951,5765,5766],{"class":953,"line":1895},[951,5767,1055],{"emptyLinePlaceholder":1054},[951,5769,5770,5773,5775],{"class":953,"line":1900},[951,5771,5772],{"class":1031},"dataset ",[951,5774,1046],{"class":1027},[951,5776,5777],{"class":1031}," Dataset.from_dict(eval_data)\n",[951,5779,5780],{"class":953,"line":1911},[951,5781,1055],{"emptyLinePlaceholder":1054},[951,5783,5784],{"class":953,"line":1926},[951,5785,5786],{"class":997},"# 运行评估\n",[951,5788,5789,5792,5794],{"class":953,"line":2622},[951,5790,5791],{"class":1031},"result ",[951,5793,1046],{"class":1027},[951,5795,5796],{"class":1031}," evaluate(\n",[951,5798,5799],{"class":953,"line":2627},[951,5800,5801],{"class":1031},"    dataset,\n",[951,5803,5804,5807,5809],{"class":953,"line":2632},[951,5805,5806],{"class":1335},"    metrics",[951,5808,1046],{"class":1027},[951,5810,5811],{"class":1031},"[faithfulness, answer_relevancy, context_precision, context_recall],\n",[951,5813,5814],{"class":953,"line":2638},[951,5815,1124],{"class":1031},[951,5817,5818,5820],{"class":953,"line":2649},[951,5819,1146],{"class":1145},[951,5821,5822],{"class":1031},"(result)\n",[940,5824,5825],{"id":5825},"简单的人工评估方法",[896,5827,5828],{},"在项目初期，可以采用简单的人工评估方式：",[5830,5831,5832,5838,5844,5850],"ol",{},[903,5833,5834,5837],{},[906,5835,5836],{},"准备测试集","：收集 20~50 个代表性问题和标准答案",[903,5839,5840,5843],{},[906,5841,5842],{},"运行系统","：对每个问题获取 RAG 系统的回答和检索结果",[903,5845,5846,5849],{},[906,5847,5848],{},"评分","：人工对回答的准确性、完整性、可读性进行 1~5 分评分",[903,5851,5852,5855],{},[906,5853,5854],{},"分析","：找出低分案例，分析是检索问题还是生成问题，有针对性地优化",[5857,5858,5859],"note",{},"\nRAG 系统的优化是一个持续迭代的过程。建议建立一套固定的评估基准（Benchmark），每次修改后都运行评估，确保改动确实提升了效果。\n",[892,5861,5862],{"id":5862},"常见问题排查",[5501,5864,5865,5878],{},[5504,5866,5867],{},[5507,5868,5869,5872,5875],{},[5510,5870,5871],{},"问题现象",[5510,5873,5874],{},"可能原因",[5510,5876,5877],{},"解决方案",[5519,5879,5880,5891,5902,5913,5924,5935],{},[5507,5881,5882,5885,5888],{},[5524,5883,5884],{},"检索结果与问题无关",[5524,5886,5887],{},"分块太大或太小",[5524,5889,5890],{},"调整 chunk_size，尝试 200~800",[5507,5892,5893,5896,5899],{},[5524,5894,5895],{},"回答包含编造内容",[5524,5897,5898],{},"Prompt 约束不足",[5524,5900,5901],{},"强化 Prompt 中\"不要编造\"的指令",[5507,5903,5904,5907,5910],{},[5524,5905,5906],{},"回答不完整",[5524,5908,5909],{},"检索数量太少",[5524,5911,5912],{},"增加 k 值，如从 3 提升到 5~8",[5507,5914,5915,5918,5921],{},[5524,5916,5917],{},"回答内容重复",[5524,5919,5920],{},"检索结果重复",[5524,5922,5923],{},"增加 chunk_overlap 或使用 MMR 检索",[5507,5925,5926,5929,5932],{},[5524,5927,5928],{},"响应速度慢",[5524,5930,5931],{},"嵌入\u002FLLM 调用慢",[5524,5933,5934],{},"使用更小的模型、添加缓存、批量处理",[5507,5936,5937,5940,5943],{},[5524,5938,5939],{},"中文效果差",[5524,5941,5942],{},"嵌入模型中文能力弱",[5524,5944,5945],{},"换用 BGE 等中文优化模型",[892,5947,5948],{"id":5948},"小结",[896,5950,5951,5952,5954],{},"通过本章的实战练习，你已经掌握了构建 RAG 系统的完整流程：从文档加载、文本分割、向量嵌入存储，到检索链构建和效果优化。RAG 技术的核心在于",[906,5953,5528],{},"——好的分块策略、合适的嵌入模型和有效的检索优化（重排序、混合检索等），是构建高质量 RAG 应用的关键。",[896,5956,5957],{},"建议在实际项目中，从最简单的基础 RAG 开始，通过评估发现问题，再逐步引入高级优化手段。",[5959,5960,5961],"style",{},"html pre.shiki code .snPdu, html code.shiki .snPdu{--shiki-light:#6F42C1;--shiki-default:#6F42C1;--shiki-dark:#B392F0}html pre.shiki code .sIIMD, html code.shiki .sIIMD{--shiki-light:#032F62;--shiki-default:#032F62;--shiki-dark:#9ECBFF}html pre.shiki code .sCsY4, html code.shiki .sCsY4{--shiki-light:#6A737D;--shiki-default:#6A737D;--shiki-dark:#6A737D}html .light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html.light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html pre.shiki code .s8jYJ, html code.shiki .s8jYJ{--shiki-light:#D73A49;--shiki-default:#D73A49;--shiki-dark:#F97583}html pre.shiki code .sxrX7, html code.shiki .sxrX7{--shiki-light:#24292E;--shiki-default:#24292E;--shiki-dark:#E1E4E8}html pre.shiki code .sBjJW, html code.shiki .sBjJW{--shiki-light:#005CC5;--shiki-default:#005CC5;--shiki-dark:#79B8FF}html pre.shiki code .sP4rz, html code.shiki .sP4rz{--shiki-light:#E36209;--shiki-default:#E36209;--shiki-dark:#FFAB70}",{"title":11,"searchDepth":977,"depth":977,"links":5963},[5964,5965,5969,5975,5979,5983,5988,5989,5995,6000,6001],{"id":894,"depth":977,"text":894},{"id":938,"depth":977,"text":938,"children":5966},[5967,5968],{"id":942,"depth":987,"text":942},{"id":1014,"depth":987,"text":1015},{"id":1073,"depth":977,"text":1074,"children":5970},[5971,5972,5973,5974],{"id":1080,"depth":987,"text":1081},{"id":1242,"depth":987,"text":1243},{"id":1288,"depth":987,"text":1288},{"id":1401,"depth":987,"text":1401},{"id":1450,"depth":977,"text":1451,"children":5976},[5977,5978],{"id":1457,"depth":987,"text":1457},{"id":1706,"depth":987,"text":1707},{"id":1965,"depth":977,"text":1966,"children":5980},[5981,5982],{"id":2121,"depth":987,"text":2121},{"id":2235,"depth":987,"text":2235},{"id":2413,"depth":977,"text":2414,"children":5984},[5985,5986,5987],{"id":2420,"depth":987,"text":2420},{"id":2908,"depth":987,"text":2909},{"id":3179,"depth":987,"text":3179},{"id":3400,"depth":977,"text":3400},{"id":4777,"depth":977,"text":4777,"children":5990},[5991,5992,5993,5994],{"id":4783,"depth":987,"text":4784},{"id":4971,"depth":987,"text":4972},{"id":5142,"depth":987,"text":5143},{"id":5303,"depth":987,"text":5304},{"id":5493,"depth":977,"text":5493,"children":5996},[5997,5998,5999],{"id":5499,"depth":987,"text":5499},{"id":5563,"depth":987,"text":5564},{"id":5825,"depth":987,"text":5825},{"id":5862,"depth":977,"text":5862},{"id":5948,"depth":977,"text":5948},"md",{},{"title":114,"description":115},"ai\u002Frag\u002Fpractice","YXkedQZ7HBq1ghRn9k4Z-XvweN8LbDZKX9XSDB4-6JY",1775474635457]