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1 |
| -import { ChartType, Column, FlowDiagram, GPTVisLite, withChartCode } from '@antv/gpt-vis'; |
| 1 | +import { ChartType, Column, FlowDiagram, GPTVisLite, MindMap, withChartCode } from '@antv/gpt-vis'; |
2 | 2 | import type { FC } from 'react';
|
3 | 3 | import React from 'react';
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4 | 4 | import type { ErrorRender } from '../type';
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@@ -39,6 +39,309 @@ const example2Markdown = `
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39 | 39 | `;
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40 | 40 |
|
41 | 41 | // 示例 - 图表渲染错误
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| 42 | +const example5Markdown = ` |
| 43 | +#### 图表渲染错误抛错 |
| 44 | +\`\`\`vis-chart |
| 45 | +{ |
| 46 | + "type": "mind-map", |
| 47 | + "data": { |
| 48 | + "name": "Transformer 模型", |
| 49 | + "children": [ |
| 50 | + { |
| 51 | + "name": "概述", |
| 52 | + "children": [ |
| 53 | + { |
| 54 | + "name": "定义与背景", |
| 55 | + "children": [ |
| 56 | + { |
| 57 | + "name": "2017年 Vaswani 等人提出" |
| 58 | + }, |
| 59 | + { |
| 60 | + "name": "自然语言处理任务" |
| 61 | + } |
| 62 | + ] |
| 63 | + }, |
| 64 | + { |
| 65 | + "name": "主要特点", |
| 66 | + "children": [ |
| 67 | + { |
| 68 | + "name": "无需循环结构" |
| 69 | + }, |
| 70 | + { |
| 71 | + "name": "依赖自注意力机制" |
| 72 | + }, |
| 73 | + { |
| 74 | + "name": "可并行处理" |
| 75 | + } |
| 76 | + ] |
| 77 | + }, |
| 78 | + { |
| 79 | + "name": "应用领域", |
| 80 | + "children": [ |
| 81 | + { |
| 82 | + "name": "机器翻译" |
| 83 | + }, |
| 84 | + { |
| 85 | + "name": "文本摘要" |
| 86 | + }, |
| 87 | + { |
| 88 | + "name": "问答系统" |
| 89 | + }, |
| 90 | + { |
| 91 | + "name": "语音处理" |
| 92 | + } |
| 93 | + ] |
| 94 | + } |
| 95 | + ] |
| 96 | + }, |
| 97 | + { |
| 98 | + "name": "核心组件", |
| 99 | + "children": [ |
| 100 | + { |
| 101 | + "name": "自注意力机制", |
| 102 | + "children": [ |
| 103 | + { |
| 104 | + "name": "机制原理", |
| 105 | + "children": [ |
| 106 | + { |
| 107 | + "name": "每个词关注其他词" |
| 108 | + }, |
| 109 | + { |
| 110 | + "name": "计算注意力权重" |
| 111 | + } |
| 112 | + ] |
| 113 | + }, |
| 114 | + { |
| 115 | + "name": "优点", |
| 116 | + "children": [ |
| 117 | + { |
| 118 | + "name": "捕捉长距离依赖" |
| 119 | + }, |
| 120 | + { |
| 121 | + "name": "可并行计算" |
| 122 | + } |
| 123 | + ] |
| 124 | + } |
| 125 | + ] |
| 126 | + }, |
| 127 | + { |
| 128 | + "name": "多头自注意力", |
| 129 | + "children": [ |
| 130 | + { |
| 131 | + "name": "多个注意力头并行计算" |
| 132 | + }, |
| 133 | + { |
| 134 | + "name": "拼接与线性变换" |
| 135 | + } |
| 136 | + ] |
| 137 | + }, |
| 138 | + { |
| 139 | + "name": "位置编码", |
| 140 | + "children": [ |
| 141 | + { |
| 142 | + "name": "作用", |
| 143 | + "children": [ |
| 144 | + { |
| 145 | + "name": "提供序列顺序信息" |
| 146 | + } |
| 147 | + ] |
| 148 | + }, |
| 149 | + { |
| 150 | + "name": "实现方式", |
| 151 | + "children": [ |
| 152 | + { |
| 153 | + "name": "正弦/余弦函数" |
| 154 | + }, |
| 155 | + { |
| 156 | + "name": "可学习的嵌入" |
| 157 | + } |
| 158 | + ] |
| 159 | + } |
| 160 | + ] |
| 161 | + }, |
| 162 | + { |
| 163 | + "name": "前馈网络", |
| 164 | + "children": [ |
| 165 | + { |
| 166 | + "name": "两层全连接网络" |
| 167 | + }, |
| 168 | + { |
| 169 | + "name": "每个位置独立处理" |
| 170 | + } |
| 171 | + ] |
| 172 | + }, |
| 173 | + { |
| 174 | + "name": "残差连接与层归一化", |
| 175 | + "children": [ |
| 176 | + { |
| 177 | + "name": "缓解梯度消失" |
| 178 | + }, |
| 179 | + { |
| 180 | + "name": "加速训练" |
| 181 | + } |
| 182 | + ] |
| 183 | + } |
| 184 | + ] |
| 185 | + }, |
| 186 | + { |
| 187 | + "name": "架构", |
| 188 | + "children": [ |
| 189 | + { |
| 190 | + "name": "编码器", |
| 191 | + "children": [ |
| 192 | + { |
| 193 | + "name": "多层结构" |
| 194 | + }, |
| 195 | + { |
| 196 | + "name": "每层组成", |
| 197 | + "children": [ |
| 198 | + { |
| 199 | + "name": "自注意力" |
| 200 | + }, |
| 201 | + { |
| 202 | + "name": "前馈网络" |
| 203 | + } |
| 204 | + ] |
| 205 | + } |
| 206 | + ] |
| 207 | + }, |
| 208 | + { |
| 209 | + "name": "解码器", |
| 210 | + "children": [ |
| 211 | + { |
| 212 | + "name": "多层结构" |
| 213 | + }, |
| 214 | + { |
| 215 | + "name": "每层组成", |
| 216 | + "children": [ |
| 217 | + { |
| 218 | + "name": "自注意力" |
| 219 | + }, |
| 220 | + { |
| 221 | + "name": "编码器-解码器注意力" |
| 222 | + }, |
| 223 | + { |
| 224 | + "name": "前馈网络" |
| 225 | + } |
| 226 | + ] |
| 227 | + } |
| 228 | + ] |
| 229 | + }, |
| 230 | + { |
| 231 | + "name": "输入输出嵌入", |
| 232 | + "children": [ |
| 233 | + { |
| 234 | + "name": "词向量转换" |
| 235 | + }, |
| 236 | + { |
| 237 | + "name": "与位置编码结合" |
| 238 | + } |
| 239 | + ] |
| 240 | + } |
| 241 | + ] |
| 242 | + }, |
| 243 | + { |
| 244 | + "name": "计算复杂度与优化", |
| 245 | + "children": [ |
| 246 | + { |
| 247 | + "name": "自注意力复杂度", |
| 248 | + "children": [ |
| 249 | + { |
| 250 | + "name": "O(N²)" |
| 251 | + } |
| 252 | + ] |
| 253 | + }, |
| 254 | + { |
| 255 | + "name": "改进方案", |
| 256 | + "children": [ |
| 257 | + { |
| 258 | + "name": "Set Transformer - 诱导点降低复杂度" |
| 259 | + }, |
| 260 | + { |
| 261 | + "name": "Reformer - 局部注意力和哈希" |
| 262 | + }, |
| 263 | + { |
| 264 | + "name": "Linformer - 降低注意力矩阵维度" |
| 265 | + }, |
| 266 | + { |
| 267 | + "name": "Longformer - 长序列局部/全局注意力" |
| 268 | + }, |
| 269 | + { |
| 270 | + "name": "Charformer - 字符级高效表示" |
| 271 | + } |
| 272 | + ] |
| 273 | + } |
| 274 | + ] |
| 275 | + }, |
| 276 | + { |
| 277 | + "name": "应用与扩展", |
| 278 | + "children": [ |
| 279 | + { |
| 280 | + "name": "自然语言处理", |
| 281 | + "children": [ |
| 282 | + { |
| 283 | + "name": "BERT、GPT 等" |
| 284 | + } |
| 285 | + ] |
| 286 | + }, |
| 287 | + { |
| 288 | + "name": "计算机视觉", |
| 289 | + "children": [ |
| 290 | + { |
| 291 | + "name": "Vision Transformer (ViT)" |
| 292 | + } |
| 293 | + ] |
| 294 | + }, |
| 295 | + { |
| 296 | + "name": "语音处理", |
| 297 | + "children": [ |
| 298 | + { |
| 299 | + "name": "Transformer 语音识别" |
| 300 | + } |
| 301 | + ] |
| 302 | + }, |
| 303 | + { |
| 304 | + "name": "强化学习", |
| 305 | + "children": [ |
| 306 | + { |
| 307 | + "name": "状态关系建模" |
| 308 | + } |
| 309 | + ] |
| 310 | + } |
| 311 | + ] |
| 312 | + }, |
| 313 | + { |
| 314 | + "name": "总结", |
| 315 | + "children": [ |
| 316 | + { |
| 317 | + "name": "优势", |
| 318 | + "children": [ |
| 319 | + { |
| 320 | + "name": "高效并行" |
| 321 | + }, |
| 322 | + { |
| 323 | + "name": "长距离依赖捕捉" |
| 324 | + } |
| 325 | + ] |
| 326 | + }, |
| 327 | + { |
| 328 | + "name": "挑战", |
| 329 | + "children": [ |
| 330 | + { |
| 331 | + "name": "大型模型计算开销" |
| 332 | + }, |
| 333 | + { |
| 334 | + "name": "需要大量训练数据" |
| 335 | + } |
| 336 | + ] |
| 337 | + } |
| 338 | + ] |
| 339 | + } |
| 340 | + ] |
| 341 | + } |
| 342 | +} |
| 343 | +`; |
| 344 | + |
42 | 345 | const example3Markdown = `
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43 | 346 | #### 默认图表渲染错误
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44 | 347 | \`\`\`vis-chart
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@@ -280,7 +583,7 @@ const CustomErrorCode2 = withChartCode({
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280 | 583 |
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281 | 584 | // 默认图表渲染错误
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282 | 585 | const DefaultChartError = withChartCode({
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283 |
| - components: { [ChartType.FlowDiagram]: FlowDiagram }, |
| 586 | + components: { [ChartType.FlowDiagram]: FlowDiagram, [ChartType.MindMap]: MindMap }, |
284 | 587 | });
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285 | 588 |
|
286 | 589 | const CustomChartError = withChartCode({
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@@ -321,6 +624,10 @@ export default () => {
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321 | 624 | <GPTVisLite components={{ code: DefaultChartError }}>{example3Markdown}</GPTVisLite>
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322 | 625 | </div>
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323 | 626 |
|
| 627 | + <div> |
| 628 | + <GPTVisLite components={{ code: DefaultChartError }}>{example5Markdown}</GPTVisLite> |
| 629 | + </div> |
| 630 | + |
324 | 631 | <div>
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325 | 632 | <GPTVisLite components={{ code: CustomChartError }}>{example4Markdown}</GPTVisLite>
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326 | 633 | </div>
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