2026-02-03 01:46:03 +08:00
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<!--
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ContextWindowVisualizer.vue
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上下文窗口可视化组件
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用途:
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直观展示 LLM 的 Context Window (上下文窗口) 限制。
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演示 Token 如何填充窗口,以及当超出限制时会发生什么(溢出/截断)。
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交互功能:
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- 文本输入:实时计算 Token 数量。
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- 预设填充:快速填充短/长文本以触发不同状态。
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- 进度条:可视化展示 Token 占用比例。
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- 溢出警告:当超出最大 Token 数时显示警告。
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-->
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<template>
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<div class="context-visualizer">
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<div class="control-panel">
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<div class="stat-group">
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<div class="stat-item">
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<span class="value" :class="{ error: isOverflow }">{{ usedTokens }}</span>
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<span class="label">已经写了多少个 token</span>
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</div>
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<div class="stat-divider">/</div>
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<div class="stat-item">
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<span class="value">{{ maxTokens }}</span>
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<span class="label">黑板最多能写几个 token</span>
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</div>
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</div>
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<div class="progress-container">
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<div class="progress-bar-bg">
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<div
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class="progress-bar-fill"
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:style="{
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width: `${Math.min(usagePercentage, 100)}%`,
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backgroundColor: progressBarColor
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}"
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></div>
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</div>
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<div class="percentage-label">{{ usagePercentage.toFixed(1) }}%</div>
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</div>
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</div>
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<div class="visualization-area">
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<div class="window-frame" :class="{ overflow: isOverflow }">
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<div class="window-header">
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<span class="icon">🧠</span>
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<span>模型能看到的“小黑板”(上下文窗口)</span>
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</div>
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<div class="token-stream">
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<transition-group name="list">
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<span
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v-for="(token, index) in tokenizedText"
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:key="index"
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class="token-chip"
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:class="getTokenClass(index)"
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>
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{{ token }}
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</span>
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</transition-group>
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</div>
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<div v-if="isOverflow" class="overflow-indicator">
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<div class="overflow-line"></div>
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<span class="overflow-text">⚠️ 达到上下文上限 (已截断)</span>
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</div>
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</div>
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</div>
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<div class="input-section">
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<div class="input-header">
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<label>输入内容(看黑板怎么被一点点写满)</label>
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<div class="actions">
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<button class="action-btn" @click="fillLorem(10)">填一段短文本</button>
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<button class="action-btn" @click="fillLorem(60)">一下子塞满黑板</button>
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<button class="action-btn outline" @click="clear">清空</button>
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</div>
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</div>
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<textarea
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v-model="inputText"
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placeholder="在这里输入几句话,看看小黑板是怎么逐渐被写满的..."
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rows="4"
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></textarea>
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</div>
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<div class="info-box">
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<p>
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<span class="icon">💡</span>
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<strong>说明:</strong>
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上下文窗口可以理解成模型的“小黑板”。黑板只有这么大,写满了就必须擦掉旧的才能写新的。
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一旦溢出,最早写的那部分内容就会被擦掉,模型会完全“看不见”它们。
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</p>
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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 } from 'vue'
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const maxTokens = 100
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const inputText = ref('上下文工程(Context Engineering)是指优化提供给大语言模型(LLM)的提示词。')
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// Simple mock tokenizer: split by space for demonstration
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// In reality, tokens are subwords, but space-split is good enough for concept
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const tokenizedText = computed(() => {
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if (!inputText.value) return []
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// Improved tokenizer:
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// 1. Matches continuous English words/numbers ([a-zA-Z0-9]+)
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// 2. OR matches any other single character (including Chinese, punctuation)
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// This provides a better visual approximation for mixed Chinese/English text
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const matches = inputText.value.match(/[a-zA-Z0-9]+|./g) || []
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return matches.filter(t => t.trim().length > 0)
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})
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const usedTokens = computed(() => tokenizedText.value.length)
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const isOverflow = computed(() => usedTokens.value > maxTokens)
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const usagePercentage = computed(() => (usedTokens.value / maxTokens) * 100)
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const progressBarColor = computed(() => {
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if (isOverflow.value) return 'var(--vp-c-danger-1)'
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if (usagePercentage.value > 80) return 'var(--vp-c-warning-1)'
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return 'var(--vp-c-success-1)'
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})
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const getTokenClass = (index) => {
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if (index >= maxTokens) return 'token-overflow'
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return `token-normal color-${index % 5}`
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}
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const fillLorem = (count) => {
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const words = [
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'人工智能', '深度学习', '神经网络', '大模型', 'Transformer', '注意力机制',
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'上下文窗口', 'Token', 'Embedding', '微调', '预训练', '推理', '生成', 'RAG'
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]
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const newText = Array.from({ length: count }, () => words[Math.floor(Math.random() * words.length)]).join(' ')
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inputText.value = newText
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}
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const clear = () => {
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inputText.value = ''
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}
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</script>
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<style scoped>
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.context-visualizer {
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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-soft);
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padding: 0.75rem;
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margin: 0.5rem 0;
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font-family: var(--vp-font-family-mono);
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}
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.control-panel {
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display: flex;
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align-items: center;
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gap: 1rem;
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margin-bottom: 1rem;
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background: var(--vp-c-bg);
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padding: 0.75rem;
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border-radius: 6px;
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border: 1px solid var(--vp-c-divider);
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}
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.stat-group {
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display: flex;
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align-items: baseline;
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gap: 0.5rem;
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}
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.stat-item {
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display: flex;
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flex-direction: column;
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align-items: center;
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}
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.stat-item .value {
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font-size: 1.2rem;
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font-weight: bold;
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line-height: 1;
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}
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.stat-item .value.error {
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color: var(--vp-c-danger-1);
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}
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.stat-item .label {
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font-size: 0.75rem;
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color: var(--vp-c-text-2);
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margin-top: 0.25rem;
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}
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.stat-divider {
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font-size: 1.5rem;
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color: var(--vp-c-divider);
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}
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.progress-container {
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flex: 1;
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display: flex;
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align-items: center;
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gap: 1rem;
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}
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.progress-bar-bg {
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flex: 1;
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height: 12px;
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background-color: var(--vp-c-bg-alt);
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border-radius: 6px;
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overflow: hidden;
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}
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.progress-bar-fill {
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height: 100%;
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transition: width 0.3s ease, background-color 0.3s ease;
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}
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.percentage-label {
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font-size: 0.9rem;
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font-weight: bold;
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width: 4rem;
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text-align: right;
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}
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.visualization-area {
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margin-bottom: 1rem;
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}
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.window-frame {
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border: 2px solid var(--vp-c-divider);
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border-radius: 6px;
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background: var(--vp-c-bg);
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min-height: 100px;
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position: relative;
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transition: border-color 0.3s;
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overflow: hidden;
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}
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.window-frame.overflow {
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border-color: var(--vp-c-danger-1);
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}
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.window-header {
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background: var(--vp-c-bg-alt);
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padding: 0.25rem 0.75rem;
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font-size: 0.85rem;
|
2026-02-03 01:46:03 +08:00
|
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font-weight: bold;
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border-bottom: 1px solid var(--vp-c-divider);
|
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display: flex;
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|
align-items: center;
|
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gap: 0.5rem;
|
2026-01-16 19:10:21 +08:00
|
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}
|
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|
.token-stream {
|
2026-02-03 19:41:14 +08:00
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padding: 0.5rem;
|
2026-01-16 19:10:21 +08:00
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display: flex;
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flex-wrap: wrap;
|
2026-02-03 19:41:14 +08:00
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gap: 2px;
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max-height: 150px;
|
2026-02-14 20:23:34 +08:00
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|
2026-01-16 19:10:21 +08:00
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}
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.token-chip {
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padding: 2px 6px;
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border-radius: 4px;
|
2026-02-03 01:46:03 +08:00
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|
font-size: 0.85rem;
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|
transition: all 0.2s;
|
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}
|
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.token-normal {
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background-color: var(--vp-c-brand-soft);
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color: var(--vp-c-brand-dark);
|
2026-01-16 19:10:21 +08:00
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}
|
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|
2026-02-03 01:46:03 +08:00
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|
/* Color cycling for tokens to show boundaries */
|
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|
.color-0 { background-color: rgba(255, 99, 132, 0.15); color: #c0392b; }
|
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.color-1 { background-color: rgba(54, 162, 235, 0.15); color: #2980b9; }
|
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.color-2 { background-color: rgba(255, 206, 86, 0.15); color: #d35400; }
|
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.color-3 { background-color: rgba(75, 192, 192, 0.15); color: #16a085; }
|
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|
.color-4 { background-color: rgba(153, 102, 255, 0.15); color: #8e44ad; }
|
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|
|
.token-overflow {
|
|
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|
|
background-color: var(--vp-c-bg-alt);
|
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|
|
color: var(--vp-c-text-3);
|
2026-01-16 19:10:21 +08:00
|
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|
|
text-decoration: line-through;
|
2026-02-03 01:46:03 +08:00
|
|
|
|
opacity: 0.6;
|
2026-01-16 19:10:21 +08:00
|
|
|
|
}
|
|
|
|
|
|
|
2026-02-03 01:46:03 +08:00
|
|
|
|
.overflow-indicator {
|
|
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|
|
|
position: absolute;
|
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|
|
|
|
bottom: 0;
|
|
|
|
|
|
left: 0;
|
|
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|
|
right: 0;
|
|
|
|
|
|
background: rgba(239, 68, 68, 0.1);
|
|
|
|
|
|
border-top: 1px dashed var(--vp-c-danger-1);
|
2026-01-16 19:10:21 +08:00
|
|
|
|
padding: 0.5rem;
|
2026-02-03 01:46:03 +08:00
|
|
|
|
display: flex;
|
|
|
|
|
|
align-items: center;
|
|
|
|
|
|
justify-content: center;
|
|
|
|
|
|
color: var(--vp-c-danger-1);
|
|
|
|
|
|
font-weight: bold;
|
|
|
|
|
|
font-size: 0.9rem;
|
2026-01-16 19:10:21 +08:00
|
|
|
|
}
|
|
|
|
|
|
|
2026-02-03 01:46:03 +08:00
|
|
|
|
.input-section {
|
|
|
|
|
|
margin-bottom: 1rem;
|
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
.input-header {
|
|
|
|
|
|
display: flex;
|
|
|
|
|
|
justify-content: space-between;
|
|
|
|
|
|
align-items: center;
|
|
|
|
|
|
margin-bottom: 0.5rem;
|
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
.input-header label {
|
|
|
|
|
|
font-size: 0.9rem;
|
|
|
|
|
|
font-weight: bold;
|
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
.actions {
|
2026-01-16 19:10:21 +08:00
|
|
|
|
display: flex;
|
|
|
|
|
|
gap: 0.5rem;
|
|
|
|
|
|
}
|
|
|
|
|
|
|
2026-02-03 01:46:03 +08:00
|
|
|
|
.action-btn {
|
|
|
|
|
|
padding: 0.25rem 0.75rem;
|
|
|
|
|
|
border-radius: 4px;
|
|
|
|
|
|
background-color: var(--vp-c-brand);
|
|
|
|
|
|
color: white;
|
|
|
|
|
|
font-size: 0.8rem;
|
|
|
|
|
|
border: none;
|
|
|
|
|
|
cursor: pointer;
|
|
|
|
|
|
transition: background-color 0.2s;
|
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
.action-btn:hover {
|
|
|
|
|
|
background-color: var(--vp-c-brand-dark);
|
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
.action-btn.outline {
|
|
|
|
|
|
background-color: transparent;
|
|
|
|
|
|
border: 1px solid var(--vp-c-divider);
|
|
|
|
|
|
color: var(--vp-c-text-1);
|
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
.action-btn.outline:hover {
|
|
|
|
|
|
border-color: var(--vp-c-brand);
|
|
|
|
|
|
color: var(--vp-c-brand);
|
|
|
|
|
|
}
|
|
|
|
|
|
|
2026-01-16 19:10:21 +08:00
|
|
|
|
textarea {
|
|
|
|
|
|
width: 100%;
|
2026-02-03 01:46:03 +08:00
|
|
|
|
padding: 0.75rem;
|
2026-01-16 19:10:21 +08:00
|
|
|
|
border: 1px solid var(--vp-c-divider);
|
2026-02-03 01:46:03 +08:00
|
|
|
|
border-radius: 6px;
|
2026-01-16 19:10:21 +08:00
|
|
|
|
background: var(--vp-c-bg);
|
|
|
|
|
|
color: var(--vp-c-text-1);
|
|
|
|
|
|
font-family: inherit;
|
|
|
|
|
|
resize: vertical;
|
|
|
|
|
|
}
|
|
|
|
|
|
|
2026-02-03 01:46:03 +08:00
|
|
|
|
textarea:focus {
|
|
|
|
|
|
outline: none;
|
|
|
|
|
|
border-color: var(--vp-c-brand);
|
2026-01-16 19:10:21 +08:00
|
|
|
|
}
|
|
|
|
|
|
|
2026-02-03 01:46:03 +08:00
|
|
|
|
.info-box {
|
|
|
|
|
|
background-color: var(--vp-c-bg-alt);
|
2026-02-14 20:23:34 +08:00
|
|
|
|
padding: 0.75rem;
|
2026-02-03 01:46:03 +08:00
|
|
|
|
border-radius: 6px;
|
|
|
|
|
|
font-size: 0.9rem;
|
|
|
|
|
|
line-height: 1.5;
|
|
|
|
|
|
color: var(--vp-c-text-2);
|
2026-01-16 19:10:21 +08:00
|
|
|
|
}
|
|
|
|
|
|
|
2026-02-03 01:46:03 +08:00
|
|
|
|
.info-box .icon {
|
|
|
|
|
|
margin-right: 0.5rem;
|
2026-01-16 19:10:21 +08:00
|
|
|
|
}
|
|
|
|
|
|
|
2026-02-03 01:46:03 +08:00
|
|
|
|
/* Animations */
|
|
|
|
|
|
.list-enter-active,
|
|
|
|
|
|
.list-leave-active {
|
2026-01-16 19:10:21 +08:00
|
|
|
|
transition: all 0.3s ease;
|
|
|
|
|
|
}
|
2026-02-03 01:46:03 +08:00
|
|
|
|
.list-enter-from,
|
|
|
|
|
|
.list-leave-to {
|
2026-01-16 19:10:21 +08:00
|
|
|
|
opacity: 0;
|
|
|
|
|
|
transform: translateY(10px);
|
|
|
|
|
|
}
|
|
|
|
|
|
</style>
|