sanbuphy
3af119a598
- 新增 Vibe Coding 全栈相关演示组件 (DeveloperSkillShift, FrontendTriad, BackendCore 等) - 新增 RAG 相关组件 (RAGPipeline, ChunkingStrategy, Retrieval 等) - 新增 Embedding & Vector 相关组件 (EmbeddingConcept, VectorSimilarity 等) - 新增 AI Native App 设计组件 (AINativeArch, PromptDesign 等) - 新增 Infrastructure as Code 组件 (IaCConcept, TerraformWorkflow 等) - 新增 DNS & HTTPS 演示组件 (DnsResolution, HttpsHandshake 等) - 新增 Model Finetuning 组件 (FinetuningPipeline 等) - 更新多个章节的 markdown 内容，集成交互式演示
418 lines
12 KiB
Vue
418 lines
12 KiB
Vue
<!--
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RAGArchitectureDemo.vue
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RAG 架构演进交互演示
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用途：
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展示三种 RAG 架构：Naive RAG、Advanced RAG、Modular RAG
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用户可以切换查看不同架构的流程图和特点。
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交互功能：
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- 切换三种架构
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- 查看每种架构的流程节点
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- 对比各架构的优劣
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-->
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<template>
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<div class="rag-arch-demo">
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<div class="arch-tabs">
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<button
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v-for="(arch, i) in architectures"
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:key="i"
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:class="['arch-tab', { active: currentArch === i }]"
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@click="currentArch = i"
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>
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<span class="tab-badge">{{ arch.badge }}</span>
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<span class="tab-name">{{ arch.name }}</span>
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</button>
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</div>
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<div class="arch-desc">
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{{ activeArch.desc }}
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</div>
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<div class="flow-diagram">
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<div
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v-for="(node, j) in activeArch.nodes"
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:key="j"
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class="flow-node-wrapper"
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>
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<div
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:class="['flow-node', node.type]"
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@click="selectedNode = selectedNode === j ? null : j"
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>
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<div class="node-icon">{{ node.icon }}</div>
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<div class="node-label">{{ node.label }}</div>
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</div>
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<div
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v-if="j < activeArch.nodes.length - 1"
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class="flow-connector"
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>
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<span class="connector-arrow">→</span>
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<span
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v-if="node.connectorLabel"
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class="connector-label"
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>{{ node.connectorLabel }}</span>
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</div>
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</div>
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</div>
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<div
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v-if="selectedNode !== null"
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class="node-detail"
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>
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<div class="node-detail-title">
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{{ activeArch.nodes[selectedNode].icon }}
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{{ activeArch.nodes[selectedNode].label }}
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</div>
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<div class="node-detail-desc">
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{{ activeArch.nodes[selectedNode].detail }}
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</div>
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</div>
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<div
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v-else
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class="node-hint"
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>
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点击流程节点查看详细说明
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</div>
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<div class="arch-features">
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<div class="feature-title">架构特点</div>
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<div class="feature-grid">
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<div
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v-for="(f, i) in activeArch.features"
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:key="i"
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class="feature-item"
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>
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<span class="feature-icon">{{ f.icon }}</span>
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<span class="feature-text">{{ f.text }}</span>
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</div>
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</div>
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</div>
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<div class="evolution-bar">
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<div class="evo-title">架构演进路线</div>
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<div class="evo-track">
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<div
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v-for="(arch, i) in architectures"
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:key="i"
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:class="['evo-node', { active: currentArch >= i }]"
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>
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<div class="evo-dot" />
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<div class="evo-label">{{ arch.name }}</div>
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<div class="evo-year">{{ arch.year }}</div>
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</div>
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<div class="evo-line">
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<div
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class="evo-line-fill"
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:style="{ width: (currentArch / (architectures.length - 1)) * 100 + '%' }"
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/>
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</div>
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</div>
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</div>
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</div>
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</template>
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<script setup>
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import { ref, computed, watch } from 'vue'
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const currentArch = ref(0)
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const selectedNode = ref(null)
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watch(currentArch, () => { selectedNode.value = null })
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const architectures = [
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{
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name: 'Naive RAG',
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badge: 'v1',
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year: '2023',
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desc: '最基础的 RAG 架构，流程简单直接：索引 → 检索 → 生成。适合快速原型验证，但在复杂场景下效果有限。',
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nodes: [
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{ icon: '📄', label: '文档加载', type: 'input', detail: '将原始文档（PDF、网页、数据库等）加载到系统中，进行基本的文本提取和清洗。', connectorLabel: '' },
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{ icon: '✂️', label: '文本分块', type: 'process', detail: '将长文档按固定大小切分为较小的文本块（chunk），通常 200-500 个 token。', connectorLabel: '' },
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{ icon: '🔢', label: '向量化', type: 'process', detail: '使用嵌入模型将每个文本块转化为向量，存入向量数据库。', connectorLabel: '' },
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{ icon: '🔍', label: '检索', type: 'process', detail: '用户提问时，将问题向量化后在向量数据库中搜索最相似的文本块。', connectorLabel: '' },
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{ icon: '🤖', label: '生成', type: 'output', detail: '将检索到的文本块与问题拼接为 Prompt，交给 LLM 生成回答。' }
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],
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features: [
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{ icon: '✅', text: '实现简单，上手快' },
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{ icon: '✅', text: '适合结构化知识库' },
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{ icon: '⚠️', text: '检索质量依赖分块策略' },
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{ icon: '❌', text: '无法处理复杂查询' }
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]
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},
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{
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name: 'Advanced RAG',
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badge: 'v2',
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year: '2024',
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desc: '在 Naive RAG 基础上增加了查询优化和检索后处理，显著提升检索质量和生成准确性。',
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nodes: [
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{ icon: '💬', label: '用户查询', type: 'input', detail: '接收用户的原始问题。', connectorLabel: '' },
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{ icon: '🔄', label: '查询改写', type: 'enhance', detail: '使用 LLM 对原始查询进行改写、扩展或分解。例如将模糊问题改写为更精确的检索查询，或生成多个子查询。', connectorLabel: '' },
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{ icon: '🔍', label: '混合检索', type: 'process', detail: '同时使用向量检索（语义）和关键词检索（BM25），融合两者的结果，兼顾语义理解和精确匹配。', connectorLabel: '' },
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{ icon: '📊', label: '重排序', type: 'enhance', detail: '使用交叉编码器对检索结果进行精细排序，过滤掉不相关的文档片段。', connectorLabel: '' },
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{ icon: '📋', label: '上下文压缩', type: 'enhance', detail: '从检索到的文档中提取与问题最相关的部分，去除冗余信息，节省上下文窗口。', connectorLabel: '' },
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{ icon: '🤖', label: '生成', type: 'output', detail: '基于优化后的上下文生成高质量回答。' }
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],
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features: [
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{ icon: '✅', text: '查询改写提升检索召回率' },
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{ icon: '✅', text: '混合检索兼顾语义和关键词' },
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{ icon: '✅', text: '重排序显著提升精度' },
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{ icon: '⚠️', text: '流程较长，延迟增加' }
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]
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},
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{
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name: 'Modular RAG',
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badge: 'v3',
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year: '2025',
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desc: '将 RAG 拆解为可插拔的模块，支持灵活组合和路由。可根据查询类型动态选择最优流程。',
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nodes: [
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{ icon: '💬', label: '用户查询', type: 'input', detail: '接收用户的原始问题。', connectorLabel: '' },
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{ icon: '🧭', label: '路由判断', type: 'enhance', detail: '分析查询意图，决定走哪条处理路径：简单问题直接回答，复杂问题走检索流程，多步问题走分解流程。', connectorLabel: '' },
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{ icon: '🔀', label: '查询转换', type: 'enhance', detail: '根据路由结果选择：HyDE（假设文档嵌入）、Step-back（退一步提问）、子问题分解等策略。', connectorLabel: '' },
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{ icon: '🔍', label: '自适应检索', type: 'process', detail: '根据查询特征自动选择检索策略：向量检索、图检索、SQL 检索或多路检索融合。', connectorLabel: '' },
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{ icon: '🔄', label: '自我反思', type: 'enhance', detail: 'LLM 评估检索结果是否充分，不充分则触发二次检索或调整检索策略（Self-RAG / CRAG）。', connectorLabel: '' },
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{ icon: '🤖', label: '生成', type: 'output', detail: '基于充分验证的上下文生成最终回答，并附带置信度评分。' }
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],
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features: [
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{ icon: '✅', text: '模块化设计，灵活可扩展' },
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{ icon: '✅', text: '自适应路由，智能选择策略' },
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{ icon: '✅', text: '自我反思机制提升可靠性' },
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{ icon: '⚠️', text: '系统复杂度高，需要精心调优' }
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]
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}
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]
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const activeArch = computed(() => architectures[currentArch.value])
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</script>
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<style scoped>
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.rag-arch-demo {
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border: 1px solid var(--vp-c-divider);
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border-radius: 12px;
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padding: 20px;
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margin: 16px 0;
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background: var(--vp-c-bg-soft);
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}
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.arch-tabs {
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display: flex;
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gap: 8px;
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margin-bottom: 16px;
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flex-wrap: wrap;
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}
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.arch-tab {
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display: flex;
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align-items: center;
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gap: 6px;
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padding: 8px 16px;
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border: 2px solid var(--vp-c-divider);
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border-radius: 8px;
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background: var(--vp-c-bg);
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cursor: pointer;
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transition: all 0.2s;
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font-size: 13px;
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}
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.arch-tab.active {
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border-color: var(--vp-c-brand-1);
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background: var(--vp-c-brand-soft);
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}
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.tab-badge {
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padding: 1px 6px;
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border-radius: 4px;
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background: var(--vp-c-divider);
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font-size: 11px;
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font-weight: 700;
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}
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.arch-tab.active .tab-badge {
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background: var(--vp-c-brand-1);
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color: #fff;
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}
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.tab-name {
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font-weight: 600;
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}
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.arch-desc {
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font-size: 13px;
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color: var(--vp-c-text-2);
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line-height: 1.6;
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margin-bottom: 16px;
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padding: 12px;
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border-radius: 8px;
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background: var(--vp-c-bg);
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border: 1px solid var(--vp-c-divider);
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}
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.flow-diagram {
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display: flex;
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align-items: center;
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gap: 0;
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overflow-x: auto;
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padding: 12px 0;
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margin-bottom: 12px;
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}
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.flow-node-wrapper {
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display: flex;
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align-items: center;
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flex-shrink: 0;
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}
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.flow-node {
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padding: 10px 14px;
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border-radius: 8px;
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border: 2px solid var(--vp-c-divider);
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background: var(--vp-c-bg);
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text-align: center;
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cursor: pointer;
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transition: all 0.2s;
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min-width: 70px;
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}
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.flow-node:hover {
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border-color: var(--vp-c-brand-1);
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transform: translateY(-2px);
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}
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.flow-node.input {
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border-color: #3b82f6;
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background: #eff6ff;
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}
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.flow-node.output {
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border-color: #10b981;
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background: #ecfdf5;
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}
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.flow-node.enhance {
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border-color: #f59e0b;
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background: #fffbeb;
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}
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.flow-node.process {
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border-color: #8b5cf6;
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background: #f5f3ff;
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}
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.node-icon {
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font-size: 20px;
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margin-bottom: 2px;
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}
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.node-label {
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font-size: 11px;
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font-weight: 600;
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color: var(--vp-c-text-1);
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}
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.flow-connector {
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display: flex;
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flex-direction: column;
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align-items: center;
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padding: 0 4px;
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}
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.connector-arrow {
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font-size: 16px;
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color: var(--vp-c-text-3);
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}
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.connector-label {
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font-size: 10px;
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color: var(--vp-c-text-3);
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}
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.node-detail {
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padding: 12px;
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border-radius: 8px;
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background: var(--vp-c-bg);
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border: 1px solid var(--vp-c-brand-1);
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margin-bottom: 16px;
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}
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.node-detail-title {
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font-weight: 600;
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font-size: 14px;
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color: var(--vp-c-brand-1);
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margin-bottom: 6px;
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}
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.node-detail-desc {
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font-size: 13px;
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color: var(--vp-c-text-2);
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line-height: 1.6;
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}
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.node-hint {
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text-align: center;
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padding: 12px;
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color: var(--vp-c-text-3);
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font-size: 13px;
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margin-bottom: 16px;
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}
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.feature-title {
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font-weight: 600;
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font-size: 14px;
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margin-bottom: 8px;
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}
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.feature-grid {
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display: grid;
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grid-template-columns: repeat(auto-fill, minmax(200px, 1fr));
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gap: 8px;
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margin-bottom: 16px;
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}
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.feature-item {
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display: flex;
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align-items: center;
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gap: 6px;
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padding: 8px 12px;
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border-radius: 6px;
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background: var(--vp-c-bg);
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font-size: 13px;
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}
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.feature-icon {
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flex-shrink: 0;
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}
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.feature-text {
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color: var(--vp-c-text-2);
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}
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.evolution-bar {
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padding: 16px;
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border-radius: 8px;
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background: var(--vp-c-bg);
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border: 1px solid var(--vp-c-divider);
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}
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.evo-title {
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font-weight: 600;
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font-size: 13px;
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margin-bottom: 16px;
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text-align: center;
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}
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.evo-track {
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display: flex;
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justify-content: space-between;
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position: relative;
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padding: 0 20px;
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}
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.evo-node {
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text-align: center;
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z-index: 1;
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opacity: 0.4;
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transition: opacity 0.3s;
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}
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.evo-node.active {
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opacity: 1;
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}
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.evo-dot {
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width: 14px;
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height: 14px;
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border-radius: 50%;
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background: var(--vp-c-divider);
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margin: 0 auto 6px;
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transition: background 0.3s;
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}
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.evo-node.active .evo-dot {
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background: var(--vp-c-brand-1);
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}
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.evo-label {
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font-size: 12px;
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font-weight: 600;
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}
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.evo-year {
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font-size: 11px;
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color: var(--vp-c-text-3);
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}
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.evo-line {
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position: absolute;
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top: 6px;
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left: 20px;
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right: 20px;
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height: 2px;
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background: var(--vp-c-divider);
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}
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.evo-line-fill {
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height: 100%;
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background: var(--vp-c-brand-1);
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transition: width 0.5s;
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}
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</style>
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