docs: update Chinese documentation and add Vue components
- Update AI capability dictionary by removing redundant mention of Baidu's model - Add new Vue components for context engineering visualization (IntroProblemReasonSolution, MemoryPalaceDemo, MemoryPalaceActionDemo, KVCacheDemo, LostInMiddleDemo) - Register new components in theme index.js - Enhance audio introduction with new interactive demos (AudioQuickStartDemo, MelSpectrogramDemo, TTSPipelineDemo, VoiceCloningDemo, ASRvsTTSDemo, AudioTokenizationDemo, EmotionControlDemo) - Improve existing context engineering demos with Chinese localization and better tokenization - Fix Japanese documentation layout by properly closing NavGrid components
This commit is contained in:
sanbuphy
+308
-413
@@ -1,11 +1,6 @@
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<!--
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* Component: RAGSimulationDemo.vue
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* Description: Demonstrates the Retrieval-Augmented Generation (RAG) process.
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* Features:
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* - Interactive search simulation
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* - Visual representation of Vector DB and Document retrieval
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* - Step-by-step animation of the RAG pipeline
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* - Visualization of context augmentation
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* Description: Demonstrates the Retrieval-Augmented Generation (RAG) process with a vertical, intuitive flow.
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-->
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<script setup>
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@@ -14,65 +9,48 @@ import { ref, computed } from 'vue'
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const query = ref('如何重置密码？')
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const lastQuery = ref('')
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const isSearching = ref(false)
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const currentStep = ref(0)
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const searchTime = ref(0)
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const currentStep = ref(0) // 0: Idle, 1: Searching/Scanning, 2: Retrieved/Assembling, 3: Done
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const documents = ref([
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{
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id: 1,
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title: '密码重置指南',
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content: '用户可以通过点击设置页面的"忘记密码"链接来重置密码。系统会发送验证邮件。',
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vector: [0.12, 0.88, 0.05],
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score: 0
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},
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{
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id: 2,
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title: '定价策略',
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content: '基础版每月 $10，专业版每月 $29。企业版需要联系销售团队获取报价。',
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vector: [0.85, 0.15, 0.10],
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score: 0
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},
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{
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id: 3,
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title: 'API 文档',
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content: '所有 API 请求都需要在 Header 中包含 Bearer Token 进行身份验证。',
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vector: [0.30, 0.20, 0.95],
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score: 0
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},
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{
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id: 4,
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title: '账户安全',
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content: '为了账户安全，建议开启双重认证 (2FA)。定期修改密码也是好习惯。',
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vector: [0.15, 0.85, 0.12],
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score: 0
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}
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])
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const steps = [
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{ id: 1, label: 'Embedding', desc: '将问题转换为向量' },
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{ id: 2, label: 'Similarity Search', desc: '计算向量相似度' },
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{ id: 3, label: 'Retrieval', desc: '提取 Top-K 相关文档' },
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{ id: 4, label: 'Augmentation', desc: '注入上下文窗口' }
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]
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const retrievedDocs = computed(() => {
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return documents.value
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.filter(doc => doc.score > 0.7)
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.filter(doc => doc.score > 0.6)
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.sort((a, b) => b.score - a.score)
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})
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const calculateSimilarity = (q, docVector) => {
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// Mock similarity calculation based on keywords for demo purposes
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// In reality, this would be a vector dot product
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if (q.includes('密码') || q.includes('安全')) {
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if (docVector[1] > 0.8) return 0.92 + (Math.random() * 0.05)
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if (docVector[0] > 0.8) return 0.15
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return 0.4 + (Math.random() * 0.1)
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}
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if (q.includes('价格') || q.includes('多少钱')) {
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if (docVector[0] > 0.8) return 0.95
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return 0.1
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}
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const calculateSimilarity = (q, docContent) => {
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// Simple keyword matching simulation
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if (q.includes('密码') && (docContent.includes('密码') || docContent.includes('安全'))) return 0.95
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if (q.includes('价格') && docContent.includes('价')) return 0.9
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if (q.includes('API') && docContent.includes('API')) return 0.9
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// Random noise for non-matches
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return Math.random() * 0.3
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}
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@@ -82,470 +60,387 @@ const search = async () => {
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isSearching.value = true
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lastQuery.value = query.value
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currentStep.value = 1
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searchTime.value = 0
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// Reset scores
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documents.value.forEach(d => d.score = 0)
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// Step 1: Embedding (Simulated)
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await new Promise(r => setTimeout(r, 800))
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currentStep.value = 2
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// Step 2: Search
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const startTime = performance.now()
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documents.value.forEach(doc => {
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doc.score = calculateSimilarity(query.value, doc.vector)
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})
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await new Promise(r => setTimeout(r, 800))
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searchTime.value = Math.round(performance.now() - startTime) + 45 // Add base latency
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currentStep.value = 3
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// Step 3: Retrieval
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await new Promise(r => setTimeout(r, 800))
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currentStep.value = 4
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// Step 1: Simulate Scanning (1.5s)
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await new Promise(r => setTimeout(r, 600))
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// Calculate scores
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documents.value.forEach(doc => {
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doc.score = calculateSimilarity(query.value, doc.content + doc.title)
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})
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await new Promise(r => setTimeout(r, 800)) // Wait for scan animation to finish visual impact
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currentStep.value = 2 // Transition to retrieval
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// Step 2: Assemble Context (1s)
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await new Promise(r => setTimeout(r, 1000))
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currentStep.value = 3 // Done
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// Step 4: Complete
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await new Promise(r => setTimeout(r, 800))
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isSearching.value = false
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}
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</script>
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<template>
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<div class="rag-simulation-demo">
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<!-- Control Panel -->
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<div class="control-panel">
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<div class="search-bar">
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<div class="rag-demo">
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<!-- Step 1: User Input -->
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<div class="step-section input-section">
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<div class="step-label">
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<span class="step-num">1</span>
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<span class="step-text">用户提问 (User Query)</span>
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</div>
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<div class="search-box">
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<input
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v-model="query"
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type="text"
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placeholder="输入问题 (例如: 怎么重置密码?)"
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placeholder="输入问题..."
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@keyup.enter="search"
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:disabled="isSearching"
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/>
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<button
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class="search-btn"
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class="action-btn"
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@click="search"
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:disabled="isSearching || !query"
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>
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{{ isSearching ? '检索中...' : '🔍 开始检索' }}
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{{ isSearching ? '检索中...' : '🚀 开始检索' }}
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</button>
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</div>
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<div class="step-indicator">
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<div
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v-for="s in steps"
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:key="s.id"
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class="step-dot"
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:class="{ active: currentStep >= s.id, current: currentStep === s.id }"
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:title="s.label"
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></div>
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</div>
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</div>
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<!-- Main Visualization -->
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<div class="viz-container">
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<!-- Arrow Connection -->
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<div class="flow-arrow" :class="{ active: currentStep >= 1 }">
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<div class="line"></div>
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<div class="icon">🔍</div>
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</div>
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<!-- Step 2: Library Scanning -->
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<div class="step-section library-section" :class="{ 'is-scanning': currentStep === 1 }">
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<div class="step-label">
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<span class="step-num">2</span>
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<span class="step-text">图书馆检索 (Retrieval)</span>
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<span class="status-badge" v-if="currentStep === 1">正在扫描...</span>
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<span class="status-badge success" v-if="currentStep >= 2">命中 {{ retrievedDocs.length }} 条</span>
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</div>
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<!-- Left: Vector Database -->
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<div class="panel vector-db" :class="{ dimmed: currentStep === 4 }">
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<div class="panel-header">
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<span class="icon">🗄️</span> 向量数据库 (Knowledge Base)
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</div>
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<div class="doc-list">
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<div
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v-for="doc in documents"
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:key="doc.id"
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class="doc-card"
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:class="{
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'scanning': currentStep === 2,
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'matched': doc.score > 0.7 && currentStep >= 3,
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'rejected': doc.score <= 0.7 && currentStep >= 3
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}"
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:style="{ '--score': doc.score }"
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>
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<div class="doc-icon">📄</div>
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<div class="doc-info">
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<div class="doc-title">{{ doc.title }}</div>
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<div class="doc-preview">{{ doc.content.substring(0, 20) }}...</div>
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</div>
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<div class="doc-score" v-if="currentStep >= 2 && doc.score > 0">
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{{ (doc.score * 100).toFixed(0) }}%
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</div>
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<div class="vector-visual">
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<span v-for="(v,i) in doc.vector" :key="i" :style="{ height: v * 10 + 'px' }"></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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<!-- Center: Pipeline Visuals -->
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<div class="pipeline-arrow">
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<div class="arrow-line" :class="{ active: isSearching }"></div>
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<div class="pipeline-status" v-if="currentStep > 0">
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{{ steps[currentStep - 1]?.label }}
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</div>
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</div>
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<!-- Right: Augmented Context -->
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<div class="panel context-window" :class="{ active: currentStep === 4 }">
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<div class="panel-header">
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<span class="icon">🤖</span> 增强后的上下文 (Final Prompt)
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</div>
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<div class="prompt-content">
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<div class="prompt-section system">
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<span class="tag">System</span>
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<p>你是一个帮助用户的 AI 助手。请基于以下上下文回答用户的问题。</p>
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<div class="docs-grid">
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<div
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v-for="doc in documents"
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:key="doc.id"
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class="doc-card"
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:class="{
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'matched': doc.score > 0.6 && currentStep >= 2,
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'ignored': doc.score <= 0.6 && currentStep >= 2
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}"
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>
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<div class="doc-header">
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<span class="doc-icon">📄</span>
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<span class="doc-title">{{ doc.title }}</span>
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<span class="doc-score" v-if="currentStep >= 2 && doc.score > 0.6">
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{{ (doc.score * 100).toFixed(0) }}% 相关
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</span>
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</div>
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<div class="doc-content">{{ doc.content }}</div>
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<div class="prompt-section context" v-if="currentStep >= 3">
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<span class="tag">Context (RAG)</span>
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<div v-if="retrievedDocs.length > 0">
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<div v-for="doc in retrievedDocs" :key="doc.id" class="retrieved-item">
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<span class="bullet">•</span> {{ doc.content }}
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</div>
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</div>
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<div v-else class="empty-context">
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(暂无相关文档)
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</div>
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</div>
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<div class="prompt-section user" v-if="lastQuery">
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<span class="tag">User</span>
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<p>{{ lastQuery }}</p>
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</div>
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<div class="placeholder-text" v-else>
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等待查询...
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</div>
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<!-- Visual effect for scanning -->
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<div class="scan-line" v-if="currentStep === 1"></div>
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</div>
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</div>
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</div>
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<!-- Metrics Footer -->
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<div class="metrics-footer">
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<div class="metric">
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<span class="label">检索耗时:</span>
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<span class="value">{{ searchTime }} ms</span>
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<!-- Arrow Connection -->
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<div class="flow-arrow" :class="{ active: currentStep >= 2 }">
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<div class="line"></div>
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<div class="icon">✂️ 复制粘贴</div>
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</div>
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<!-- Step 3: Context Assembly -->
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<div class="step-section context-section" :class="{ active: currentStep >= 3 }">
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<div class="step-label">
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<span class="step-num">3</span>
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<span class="step-text">最终上下文 (Final Prompt)</span>
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</div>
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<div class="metric">
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<span class="label">命中数量:</span>
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<span class="value">{{ retrievedDocs.length }} docs</span>
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<div class="blackboard">
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<div class="chalk-text system">
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<span class="role-badge">SYSTEM</span>
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你是一个专业的 AI 助手。请基于下方【检索到的资料】回答用户的提问。
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</div>
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<div class="retrieved-block" v-if="currentStep >= 2">
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<div class="block-header">📚 检索到的资料 (Context)</div>
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<div v-if="retrievedDocs.length > 0">
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<div v-for="doc in retrievedDocs" :key="doc.id" class="retrieved-item">
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{{ doc.content }}
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</div>
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</div>
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<div v-else class="empty-state">
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(未找到相关资料)
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</div>
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</div>
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<div class="chalk-text user">
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<span class="role-badge">USER</span>
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{{ lastQuery || '等待提问...' }}
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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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<style scoped>
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.rag-simulation-demo {
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border: 1px solid var(--vp-c-divider);
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border-radius: 8px;
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background-color: var(--vp-c-bg-soft);
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overflow: hidden;
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margin: 1rem 0;
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.rag-demo {
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display: flex;
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flex-direction: column;
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gap: 0;
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background: var(--vp-c-bg-soft);
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border: 1px solid var(--vp-c-divider);
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border-radius: 12px;
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padding: 1.5rem;
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max-width: 600px;
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margin: 1rem auto;
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}
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.control-panel {
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.step-section {
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position: relative;
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background: var(--vp-c-bg);
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border: 1px solid var(--vp-c-divider);
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border-radius: 8px;
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padding: 1rem;
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background-color: var(--vp-c-bg);
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border-bottom: 1px solid var(--vp-c-divider);
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display: flex;
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justify-content: space-between;
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align-items: center;
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flex-wrap: wrap;
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gap: 1rem;
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transition: all 0.3s ease;
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}
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.search-bar {
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.step-label {
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display: flex;
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align-items: center;
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gap: 0.5rem;
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margin-bottom: 0.8rem;
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font-weight: 600;
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font-size: 0.95rem;
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||||
color: var(--vp-c-text-1);
|
||||
}
|
||||
|
||||
.step-num {
|
||||
background: var(--vp-c-brand);
|
||||
color: white;
|
||||
width: 20px;
|
||||
height: 20px;
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border-radius: 50%;
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||||
display: flex;
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align-items: center;
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||||
justify-content: center;
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font-size: 0.75rem;
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font-weight: bold;
|
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}
|
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|
||||
/* Input Section */
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.search-box {
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||||
display: flex;
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gap: 0.5rem;
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flex: 1;
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min-width: 280px;
|
||||
}
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|
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input {
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||||
flex: 1;
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||||
padding: 0.5rem 0.8rem;
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padding: 0.6rem;
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||||
border: 1px solid var(--vp-c-divider);
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||||
border-radius: 6px;
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||||
background-color: var(--vp-c-bg-alt);
|
||||
color: var(--vp-c-text-1);
|
||||
}
|
||||
input:focus {
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||||
border-color: var(--vp-c-brand);
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||||
outline: none;
|
||||
background: var(--vp-c-bg-alt);
|
||||
font-size: 0.9rem;
|
||||
}
|
||||
|
||||
.search-btn {
|
||||
padding: 0.5rem 1rem;
|
||||
background-color: var(--vp-c-brand);
|
||||
.action-btn {
|
||||
background: var(--vp-c-brand);
|
||||
color: white;
|
||||
padding: 0.5rem 1rem;
|
||||
border-radius: 6px;
|
||||
font-weight: 500;
|
||||
transition: background-color 0.2s;
|
||||
transition: opacity 0.2s;
|
||||
}
|
||||
.search-btn:hover:not(:disabled) {
|
||||
background-color: var(--vp-c-brand-dark);
|
||||
}
|
||||
.search-btn:disabled {
|
||||
.action-btn:disabled {
|
||||
opacity: 0.6;
|
||||
cursor: not-allowed;
|
||||
}
|
||||
|
||||
.step-indicator {
|
||||
display: flex;
|
||||
gap: 0.4rem;
|
||||
}
|
||||
.step-dot {
|
||||
width: 10px;
|
||||
height: 10px;
|
||||
border-radius: 50%;
|
||||
background-color: var(--vp-c-divider);
|
||||
transition: all 0.3s;
|
||||
}
|
||||
.step-dot.active {
|
||||
background-color: var(--vp-c-brand);
|
||||
}
|
||||
.step-dot.current {
|
||||
transform: scale(1.4);
|
||||
box-shadow: 0 0 4px var(--vp-c-brand);
|
||||
}
|
||||
|
||||
/* Viz Container */
|
||||
.viz-container {
|
||||
display: flex;
|
||||
padding: 1.5rem;
|
||||
gap: 1rem;
|
||||
background-color: var(--vp-c-bg-alt);
|
||||
min-height: 350px;
|
||||
align-items: stretch;
|
||||
}
|
||||
|
||||
.panel {
|
||||
flex: 1;
|
||||
background-color: var(--vp-c-bg);
|
||||
border: 1px solid var(--vp-c-divider);
|
||||
border-radius: 8px;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
transition: all 0.5s ease;
|
||||
}
|
||||
.panel.dimmed {
|
||||
opacity: 0.6;
|
||||
filter: grayscale(0.5);
|
||||
}
|
||||
.panel.active {
|
||||
border-color: var(--vp-c-brand);
|
||||
box-shadow: 0 0 15px rgba(var(--vp-c-brand-rgb), 0.1);
|
||||
}
|
||||
|
||||
.panel-header {
|
||||
padding: 0.8rem;
|
||||
font-weight: 600;
|
||||
font-size: 0.9rem;
|
||||
border-bottom: 1px solid var(--vp-c-divider);
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 0.5rem;
|
||||
background-color: var(--vp-c-bg-soft);
|
||||
}
|
||||
|
||||
.doc-list {
|
||||
padding: 0.8rem;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 0.6rem;
|
||||
overflow-y: auto;
|
||||
max-height: 300px;
|
||||
/* Library Section */
|
||||
.docs-grid {
|
||||
display: grid;
|
||||
grid-template-columns: repeat(auto-fill, minmax(180px, 1fr));
|
||||
gap: 0.8rem;
|
||||
}
|
||||
|
||||
.doc-card {
|
||||
padding: 0.6rem;
|
||||
background: var(--vp-c-bg-alt);
|
||||
border: 1px solid var(--vp-c-divider);
|
||||
border-radius: 6px;
|
||||
padding: 0.6rem;
|
||||
font-size: 0.8rem;
|
||||
position: relative;
|
||||
overflow: hidden;
|
||||
transition: all 0.3s ease;
|
||||
}
|
||||
|
||||
.doc-card.matched {
|
||||
border-color: var(--vp-c-brand);
|
||||
background: var(--vp-c-brand-dimm);
|
||||
transform: scale(1.02);
|
||||
box-shadow: 0 2px 8px rgba(0,0,0,0.1);
|
||||
}
|
||||
|
||||
.doc-card.ignored {
|
||||
opacity: 0.4;
|
||||
filter: grayscale(0.8);
|
||||
}
|
||||
|
||||
.doc-header {
|
||||
display: flex;
|
||||
justify-content: space-between;
|
||||
align-items: center;
|
||||
margin-bottom: 0.4rem;
|
||||
font-weight: 600;
|
||||
}
|
||||
|
||||
.doc-score {
|
||||
color: var(--vp-c-brand);
|
||||
font-size: 0.75rem;
|
||||
}
|
||||
|
||||
.doc-content {
|
||||
color: var(--vp-c-text-2);
|
||||
line-height: 1.4;
|
||||
display: -webkit-box;
|
||||
-webkit-line-clamp: 2;
|
||||
-webkit-box-orient: vertical;
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
/* Scanning Animation */
|
||||
.scan-line {
|
||||
position: absolute;
|
||||
top: 0;
|
||||
left: -100%;
|
||||
width: 50%;
|
||||
height: 100%;
|
||||
background: linear-gradient(90deg, transparent, rgba(255,255,255,0.2), transparent);
|
||||
animation: scan 1s infinite;
|
||||
pointer-events: none;
|
||||
}
|
||||
|
||||
@keyframes scan {
|
||||
0% { left: -100%; }
|
||||
100% { left: 200%; }
|
||||
}
|
||||
|
||||
/* Context Section */
|
||||
.blackboard {
|
||||
background: #2b2b2b;
|
||||
color: #e0e0e0;
|
||||
padding: 1rem;
|
||||
border-radius: 6px;
|
||||
font-family: 'Menlo', 'Monaco', 'Courier New', monospace;
|
||||
font-size: 0.85rem;
|
||||
line-height: 1.5;
|
||||
border: 2px solid #444;
|
||||
}
|
||||
|
||||
.role-badge {
|
||||
display: inline-block;
|
||||
background: #444;
|
||||
color: #aaa;
|
||||
padding: 1px 4px;
|
||||
border-radius: 3px;
|
||||
font-size: 0.7rem;
|
||||
margin-right: 6px;
|
||||
vertical-align: middle;
|
||||
}
|
||||
|
||||
.chalk-text {
|
||||
margin-bottom: 0.8rem;
|
||||
}
|
||||
|
||||
.retrieved-block {
|
||||
background: rgba(255, 255, 255, 0.1);
|
||||
border-left: 3px solid var(--vp-c-brand);
|
||||
padding: 0.6rem;
|
||||
margin: 0.5rem 0 1rem 0;
|
||||
animation: slideIn 0.5s ease-out;
|
||||
}
|
||||
|
||||
@keyframes slideIn {
|
||||
from { opacity: 0; transform: translateY(-10px); }
|
||||
to { opacity: 1; transform: translateY(0); }
|
||||
}
|
||||
|
||||
.block-header {
|
||||
color: var(--vp-c-brand);
|
||||
font-weight: bold;
|
||||
font-size: 0.75rem;
|
||||
margin-bottom: 0.4rem;
|
||||
text-transform: uppercase;
|
||||
}
|
||||
|
||||
.retrieved-item {
|
||||
margin-bottom: 0.4rem;
|
||||
padding-left: 0.8rem;
|
||||
position: relative;
|
||||
}
|
||||
.retrieved-item::before {
|
||||
content: "•";
|
||||
position: absolute;
|
||||
left: 0;
|
||||
color: #888;
|
||||
}
|
||||
|
||||
/* Arrows */
|
||||
.flow-arrow {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 0.6rem;
|
||||
font-size: 0.85rem;
|
||||
justify-content: center;
|
||||
height: 40px;
|
||||
color: var(--vp-c-divider);
|
||||
position: relative;
|
||||
transition: all 0.3s;
|
||||
background-color: var(--vp-c-bg);
|
||||
}
|
||||
|
||||
.doc-card.scanning {
|
||||
animation: pulse 1s infinite;
|
||||
border-color: var(--vp-c-brand-dimm);
|
||||
}
|
||||
.doc-card.matched {
|
||||
border-color: var(--vp-c-green);
|
||||
background-color: var(--vp-c-green-dimm);
|
||||
transform: translateX(5px);
|
||||
}
|
||||
.doc-card.rejected {
|
||||
opacity: 0.5;
|
||||
.flow-arrow .line {
|
||||
position: absolute;
|
||||
height: 100%;
|
||||
width: 2px;
|
||||
background: var(--vp-c-divider);
|
||||
z-index: 0;
|
||||
}
|
||||
|
||||
.doc-icon {
|
||||
.flow-arrow .icon {
|
||||
background: var(--vp-c-bg-soft);
|
||||
padding: 4px;
|
||||
z-index: 1;
|
||||
font-size: 1.2rem;
|
||||
}
|
||||
.doc-info {
|
||||
flex: 1;
|
||||
overflow: hidden;
|
||||
|
||||
.flow-arrow.active .line {
|
||||
background: var(--vp-c-brand);
|
||||
}
|
||||
.doc-title {
|
||||
font-weight: 600;
|
||||
color: var(--vp-c-text-1);
|
||||
.flow-arrow.active .icon {
|
||||
animation: bounce 1s infinite;
|
||||
}
|
||||
.doc-preview {
|
||||
color: var(--vp-c-text-2);
|
||||
|
||||
.status-badge {
|
||||
font-size: 0.75rem;
|
||||
white-space: nowrap;
|
||||
overflow: hidden;
|
||||
text-overflow: ellipsis;
|
||||
}
|
||||
.doc-score {
|
||||
font-family: var(--vp-font-mono);
|
||||
font-weight: bold;
|
||||
color: var(--vp-c-brand);
|
||||
}
|
||||
.vector-visual {
|
||||
display: flex;
|
||||
gap: 2px;
|
||||
align-items: flex-end;
|
||||
height: 15px;
|
||||
width: 20px;
|
||||
}
|
||||
.vector-visual span {
|
||||
width: 4px;
|
||||
background-color: var(--vp-c-text-3);
|
||||
border-radius: 1px;
|
||||
}
|
||||
|
||||
/* Pipeline Arrow */
|
||||
.pipeline-arrow {
|
||||
width: 40px;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
justify-content: center;
|
||||
align-items: center;
|
||||
position: relative;
|
||||
}
|
||||
.arrow-line {
|
||||
width: 100%;
|
||||
height: 4px;
|
||||
background-color: var(--vp-c-divider);
|
||||
border-radius: 2px;
|
||||
transition: all 0.3s;
|
||||
}
|
||||
.arrow-line.active {
|
||||
background: linear-gradient(90deg, var(--vp-c-brand), var(--vp-c-brand-light));
|
||||
background-size: 200% 100%;
|
||||
animation: flow 1s linear infinite;
|
||||
}
|
||||
.pipeline-status {
|
||||
position: absolute;
|
||||
top: 40%;
|
||||
left: 50%;
|
||||
transform: translate(-50%, -50%);
|
||||
background-color: var(--vp-c-brand);
|
||||
color: white;
|
||||
padding: 0.2rem 0.6rem;
|
||||
border-radius: 12px;
|
||||
font-size: 0.7rem;
|
||||
white-space: nowrap;
|
||||
z-index: 10;
|
||||
}
|
||||
|
||||
/* Context Window */
|
||||
.prompt-content {
|
||||
padding: 1rem;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 1rem;
|
||||
font-family: var(--vp-font-mono);
|
||||
font-size: 0.85rem;
|
||||
overflow-y: auto;
|
||||
}
|
||||
|
||||
.prompt-section {
|
||||
background-color: var(--vp-c-bg-soft);
|
||||
padding: 0.8rem;
|
||||
border-radius: 6px;
|
||||
border-left: 3px solid transparent;
|
||||
}
|
||||
.prompt-section.system {
|
||||
border-left-color: var(--vp-c-yellow);
|
||||
}
|
||||
.prompt-section.context {
|
||||
border-left-color: var(--vp-c-green);
|
||||
background-color: rgba(var(--vp-c-green-rgb), 0.1);
|
||||
}
|
||||
.prompt-section.user {
|
||||
border-left-color: var(--vp-c-brand);
|
||||
}
|
||||
|
||||
.tag {
|
||||
display: inline-block;
|
||||
font-size: 0.7rem;
|
||||
font-weight: bold;
|
||||
text-transform: uppercase;
|
||||
margin-bottom: 0.4rem;
|
||||
padding: 2px 6px;
|
||||
border-radius: 4px;
|
||||
background: var(--vp-c-bg-soft);
|
||||
color: var(--vp-c-text-2);
|
||||
}
|
||||
.retrieved-item {
|
||||
margin-top: 0.4rem;
|
||||
color: var(--vp-c-text-1);
|
||||
}
|
||||
.empty-context {
|
||||
color: var(--vp-c-text-3);
|
||||
font-style: italic;
|
||||
text-align: center;
|
||||
}
|
||||
.placeholder-text {
|
||||
text-align: center;
|
||||
color: var(--vp-c-text-3);
|
||||
margin-top: 2rem;
|
||||
|
||||
.status-badge.success {
|
||||
background: rgba(16, 185, 129, 0.1);
|
||||
color: #10b981;
|
||||
}
|
||||
|
||||
/* Metrics Footer */
|
||||
.metrics-footer {
|
||||
display: flex;
|
||||
justify-content: space-around;
|
||||
padding: 0.8rem;
|
||||
background-color: var(--vp-c-bg);
|
||||
border-top: 1px solid var(--vp-c-divider);
|
||||
font-size: 0.85rem;
|
||||
}
|
||||
.metric .label {
|
||||
color: var(--vp-c-text-2);
|
||||
margin-right: 0.5rem;
|
||||
}
|
||||
.metric .value {
|
||||
font-weight: bold;
|
||||
color: var(--vp-c-text-1);
|
||||
}
|
||||
|
||||
@keyframes flow {
|
||||
0% { background-position: 100% 0; }
|
||||
100% { background-position: -100% 0; }
|
||||
}
|
||||
@keyframes pulse {
|
||||
0% { opacity: 1; }
|
||||
50% { opacity: 0.6; }
|
||||
100% { opacity: 1; }
|
||||
}
|
||||
|
||||
@media (max-width: 768px) {
|
||||
.viz-container {
|
||||
flex-direction: column;
|
||||
}
|
||||
.pipeline-arrow {
|
||||
width: 100%;
|
||||
height: 40px;
|
||||
flex-direction: row;
|
||||
}
|
||||
.arrow-line {
|
||||
width: 4px;
|
||||
height: 100%;
|
||||
}
|
||||
@keyframes bounce {
|
||||
0%, 100% { transform: translateY(0); }
|
||||
50% { transform: translateY(3px); }
|
||||
}
|
||||
</style>
|
||||
|
||||
Reference in New Issue
Block a user