chore: enrich demo fixtures and fix pages trial links

Author: ly-xxx

README.md

@@ -5,9 +5,9 @@
 # OfferPotato
 
 <p align="center">
-  <a href="https://github.com/ly-xxx/Offer-loom/stargazers"><img src="https://img.shields.io/github/stars/ly-xxx/Offer-loom?style=flat-square" alt="GitHub stars" /></a>
-  <a href="https://github.com/ly-xxx/Offer-loom/network/members"><img src="https://img.shields.io/github/forks/ly-xxx/Offer-loom?style=flat-square" alt="GitHub forks" /></a>
-  <a href="https://github.com/ly-xxx/Offer-loom/issues"><img src="https://img.shields.io/github/issues/ly-xxx/Offer-loom?style=flat-square" alt="GitHub issues" /></a>
+  <a href="https://github.com/ly-xxx/Offer-Patato/stargazers"><img src="https://img.shields.io/github/stars/ly-xxx/Offer-Patato?style=flat-square" alt="GitHub stars" /></a>
+  <a href="https://github.com/ly-xxx/Offer-Patato/network/members"><img src="https://img.shields.io/github/forks/ly-xxx/Offer-Patato?style=flat-square" alt="GitHub forks" /></a>
+  <a href="https://github.com/ly-xxx/Offer-Patato/issues"><img src="https://img.shields.io/github/issues/ly-xxx/Offer-Patato?style=flat-square" alt="GitHub issues" /></a>
   <a href="./LICENSE"><img src="https://img.shields.io/badge/license-MIT-16a34a?style=flat-square" alt="MIT license" /></a>
 </p>
 
@@ -28,13 +28,13 @@
 </p>
 
 <p align="center">
-  <a href="https://ly-xxx.github.io/Offer-loom/">
+  <a href="https://ly-xxx.github.io/Offer-Patato/">
     <img src="https://img.shields.io/badge/ONLINE%20TRY%20NOW-GitHub%20Pages-ff5a1f?style=for-the-badge&logo=githubpages&logoColor=white" alt="Try OfferPotato Demo" />
   </a>
 </p>
 
 <p align="center">
-  <strong><a href="https://ly-xxx.github.io/Offer-loom/">在线试用入口</a></strong>（无需本地安装）
+  <strong><a href="https://ly-xxx.github.io/Offer-Patato/">在线试用入口</a></strong>（无需本地安装）
 </p>
 
 **如果你正在准备技术面试，大概率会遇到这三个死局：**
@@ -69,8 +69,8 @@ OfferPotato 不绑定特定技术栈。无论你是准备 LLM、前端、后端
 仓库内置了公开的测试数据，即便你还没整理好自己的资料，也可以直接跑起来体验整套界面和工作流。
 
 ```bash
-git clone https://github.com/ly-xxx/Offer-loom.git
-cd Offer-loom
+git clone https://github.com/ly-xxx/Offer-Patato.git
+cd Offer-Patato
 npm install
 npm run setup:serve
 ```
@@ -195,10 +195,10 @@ sources/
 
 ## Star History
 
-<a href="https://www.star-history.com/#ly-xxx/Offer-loom&Date">
+<a href="https://www.star-history.com/#ly-xxx/Offer-Patato&Date">
   <picture>
-    <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=ly-xxx/Offer-loom&type=Date&theme=dark" />
-    <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=ly-xxx/Offer-loom&type=Date" />
-    <img alt="Star History Chart" src="https://api.star-history.com/svg?repos=ly-xxx/Offer-loom&type=Date" />
+    <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=ly-xxx/Offer-Patato&type=Date&theme=dark" />
+    <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=ly-xxx/Offer-Patato&type=Date" />
+    <img alt="Star History Chart" src="https://api.star-history.com/svg?repos=ly-xxx/Offer-Patato&type=Date" />
   </picture>
 </a>

web/public/demo-api/documents.json

@@ -1,14 +1,25 @@
 [
   {
-    "id": "doc-guide-llm",
+    "id": "doc-guide-rag-playbook",
     "kind": "guide",
     "ext": "md",
-    "path": "sources/documents/demo/llm-guide.md",
-    "relPath": "demo/llm-guide.md",
+    "path": "sources/documents/demo/rag-playbook.md",
+    "relPath": "demo/rag-playbook.md",
     "sourceId": "demo-guide-source",
-    "title": "LLM 与 RAG 面试主线（Demo）",
-    "updatedAt": "2026-03-27T09:40:00.000Z",
-    "knowledgeHitCount": 3
+    "title": "RAG 架构与检索策略（Demo）",
+    "updatedAt": "2026-03-27T09:30:00.000Z",
+    "knowledgeHitCount": 7
+  },
+  {
+    "id": "doc-guide-rag-eval",
+    "kind": "guide",
+    "ext": "md",
+    "path": "sources/documents/demo/rag-eval-and-ops.md",
+    "relPath": "demo/rag-eval-and-ops.md",
+    "sourceId": "demo-guide-source",
+    "title": "RAG 评估、上线与运维（Demo）",
+    "updatedAt": "2026-03-27T09:36:00.000Z",
+    "knowledgeHitCount": 6
   },
   {
     "id": "doc-work-offerloom",
@@ -18,7 +29,40 @@
     "relPath": "offerloom/architecture-notes.md",
     "sourceId": "demo-mywork",
     "title": "OfferLoom 架构笔记（Demo）",
-    "updatedAt": "2026-03-27T09:45:00.000Z",
-    "knowledgeHitCount": 2
+    "updatedAt": "2026-03-27T11:35:00.000Z",
+    "knowledgeHitCount": 9
+  },
+  {
+    "id": "doc-work-rag-architecture",
+    "kind": "work",
+    "ext": "md",
+    "path": "mywork/rag-compass/architecture/overview.md",
+    "relPath": "rag-compass/architecture/overview.md",
+    "sourceId": "demo-mywork",
+    "title": "RAGCompass 架构设计（Demo）",
+    "updatedAt": "2026-03-27T09:48:00.000Z",
+    "knowledgeHitCount": 5
+  },
+  {
+    "id": "doc-work-rag-evaluation",
+    "kind": "work",
+    "ext": "md",
+    "path": "mywork/rag-compass/eval/offline-report.md",
+    "relPath": "rag-compass/eval/offline-report.md",
+    "sourceId": "demo-mywork",
+    "title": "RAGCompass 离线评估报告（Demo）",
+    "updatedAt": "2026-03-27T09:52:00.000Z",
+    "knowledgeHitCount": 4
+  },
+  {
+    "id": "doc-work-rag-ops",
+    "kind": "work",
+    "ext": "md",
+    "path": "mywork/rag-compass/ops/indexing-runbook.md",
+    "relPath": "rag-compass/ops/indexing-runbook.md",
+    "sourceId": "demo-mywork",
+    "title": "RAGCompass 索引运维 Runbook（Demo）",
+    "updatedAt": "2026-03-27T09:55:00.000Z",
+    "knowledgeHitCount": 4
   }
 ]

web/public/demo-api/documents/doc-guide-rag-eval.json

@@ -0,0 +1,155 @@
+{
+  "id": "doc-guide-rag-eval",
+  "kind": "guide",
+  "ext": "md",
+  "path": "sources/documents/demo/rag-eval-and-ops.md",
+  "relPath": "demo/rag-eval-and-ops.md",
+  "sourceId": "demo-guide-source",
+  "title": "RAG 评估、上线与运维（Demo）",
+  "updatedAt": "2026-03-27T09:36:00.000Z",
+  "watchPath": null,
+  "meta": {},
+  "content": "# RAG 评估、上线与运维\n\n## 质量评估框架\n采用 Recall@k、nDCG、Groundedness、Hallucination rate 四维评估。\n\n## 多租户权限模型\n租户级索引隔离 + 文档 ACL + 查询时二次过滤。\n\n## 索引与回滚\n增量批次采用两阶段提交，失败时按批次号回滚并重放。",
+  "sections": [
+    {
+      "anchor": "eval-framework",
+      "heading": "质量评估框架",
+      "level": 2,
+      "startLine": 3,
+      "endLine": 6,
+      "knowledgeHitCount": 2,
+      "content": "采用 Recall@k、nDCG、Groundedness、Hallucination rate 四维评估。",
+      "relatedQuestions": [
+        {
+          "id": "demo-q-6",
+          "text": "为什么你们选 cross-encoder reranker，不是只用向量相似度？",
+          "displayText": "为什么你们选 cross-encoder reranker，不是只用向量相似度？",
+          "difficulty": "medium",
+          "score": 0.9,
+          "questionType": "evaluation",
+          "translatedText": "为什么你们选 cross-encoder reranker，不是只用向量相似度？",
+          "isRevisited": false,
+          "generatedCount": 1,
+          "generatedStatus": "ready",
+          "lastGeneratedAt": "2026-03-27T10:10:00.000Z",
+          "generated": {
+            "elevator_pitch": "相关性质量提升显著。",
+            "full_answer_markdown": "Cross-encoder 在 hard negative 上明显优于单纯向量相似度。",
+            "work_story": "我们离线 A/B 中 MRR 提升 12%。",
+            "work_evidence_status": "direct",
+            "follow_ups": ["延迟增加怎么补？", "是否蒸馏轻量模型？"],
+            "missing_basics": []
+          }
+        },
+        {
+          "id": "demo-q-7",
+          "text": "你们怎么评估 hallucination 和 groundedness？",
+          "displayText": "你们怎么评估 hallucination 和 groundedness？",
+          "difficulty": "hard",
+          "score": 0.94,
+          "questionType": "evaluation",
+          "translatedText": "你们怎么评估 hallucination 和 groundedness？",
+          "isRevisited": false,
+          "generatedCount": 1,
+          "generatedStatus": "ready",
+          "lastGeneratedAt": "2026-03-27T10:12:00.000Z",
+          "generated": {
+            "elevator_pitch": "引用一致性 + 人审抽检双轨。",
+            "full_answer_markdown": "自动评测看引用命中和语义一致性，再做人审抽检闭环。",
+            "work_story": "RAGCompass 每周固定抽检 200 条问答。",
+            "work_evidence_status": "adjacent",
+            "follow_ups": ["阈值怎么设？", "误报如何处理？"],
+            "missing_basics": []
+          }
+        }
+      ]
+    },
+    {
+      "anchor": "tenant-acl",
+      "heading": "多租户权限模型",
+      "level": 2,
+      "startLine": 8,
+      "endLine": 11,
+      "knowledgeHitCount": 2,
+      "content": "租户级索引隔离 + 文档 ACL + 查询时二次过滤。",
+      "relatedQuestions": [
+        {
+          "id": "demo-q-8",
+          "text": "多租户文档怎么做权限隔离，避免串库？",
+          "displayText": "多租户文档怎么做权限隔离，避免串库？",
+          "difficulty": "hard",
+          "score": 0.92,
+          "questionType": "security",
+          "translatedText": "多租户文档怎么做权限隔离，避免串库？",
+          "isRevisited": false,
+          "generatedCount": 1,
+          "generatedStatus": "ready",
+          "lastGeneratedAt": "2026-03-27T10:14:00.000Z",
+          "generated": {
+            "elevator_pitch": "双重过滤保证安全边界。",
+            "full_answer_markdown": "索引分租户隔离，召回后再走 ACL 过滤并记录审计日志。",
+            "work_story": "我们在网关和检索层都做了 tenant_id 校验。",
+            "work_evidence_status": "direct",
+            "follow_ups": ["跨租户共享文档如何做？", "审计日志保留多久？"],
+            "missing_basics": []
+          }
+        }
+      ]
+    },
+    {
+      "anchor": "index-rollback",
+      "heading": "索引与回滚",
+      "level": 2,
+      "startLine": 13,
+      "endLine": 16,
+      "knowledgeHitCount": 2,
+      "content": "增量批次采用两阶段提交，失败时按批次号回滚并重放。",
+      "relatedQuestions": [
+        {
+          "id": "demo-q-9",
+          "text": "索引增量更新失败时怎么回滚和重放？",
+          "displayText": "索引增量更新失败时怎么回滚和重放？",
+          "difficulty": "medium",
+          "score": 0.93,
+          "questionType": "ops",
+          "translatedText": "索引增量更新失败时怎么回滚和重放？",
+          "isRevisited": false,
+          "generatedCount": 1,
+          "generatedStatus": "ready",
+          "lastGeneratedAt": "2026-03-27T10:16:00.000Z",
+          "generated": {
+            "elevator_pitch": "批次化提交 + 可重放日志。",
+            "full_answer_markdown": "每个批次带版本号，提交失败回滚到上个稳定版本并重放事件流。",
+            "work_story": "RAGCompass 夜间增量任务失败后可 10 分钟恢复。",
+            "work_evidence_status": "direct",
+            "follow_ups": ["重放幂等怎么保证？", "回滚期间如何服务？"],
+            "missing_basics": []
+          }
+        }
+      ]
+    }
+  ],
+  "looseRelatedQuestions": [
+    {
+      "id": "demo-q-10",
+      "text": "RAG 场景下怎么做 token 成本优化？",
+      "displayText": "RAG 场景下怎么做 token 成本优化？",
+      "difficulty": "medium",
+      "score": 0.77,
+      "questionType": "cost",
+      "translatedText": "RAG 场景下怎么做 token 成本优化？",
+      "isRevisited": true,
+      "generatedCount": 1,
+      "generatedStatus": "ready",
+      "lastGeneratedAt": "2026-03-27T10:18:00.000Z",
+      "generated": {
+        "elevator_pitch": "缓存、压缩和分级模型。",
+        "full_answer_markdown": "通过 query cache、摘要压缩和模型分级路由可以显著降本。",
+        "work_story": "RAGCompass 在重复查询上命中缓存后成本下降 37%。",
+        "work_evidence_status": "adjacent",
+        "follow_ups": ["缓存一致性怎么做？"],
+        "missing_basics": []
+      }
+    }
+  ]
+}

web/public/demo-api/documents/doc-guide-rag-playbook.json

@@ -0,0 +1,112 @@
+{
+  "id": "doc-guide-rag-playbook",
+  "kind": "guide",
+  "ext": "md",
+  "path": "sources/documents/demo/rag-playbook.md",
+  "relPath": "demo/rag-playbook.md",
+  "sourceId": "demo-guide-source",
+  "title": "RAG 架构与检索策略（Demo）",
+  "updatedAt": "2026-03-27T09:30:00.000Z",
+  "watchPath": null,
+  "meta": {},
+  "content": "# RAG 架构与检索策略\n\n## 架构总览\n采用 Query Rewrite + Hybrid Retrieval + Cross-Encoder Rerank + Citation Guardrail。\n\n## 召回策略\n第一阶段向量召回 80 条 + BM25 40 条，融合去重后进入重排。\n\n## Chunk 方案\n按语义段 + 结构标题双切片，窗口 420 token，重叠 80 token。",
+  "sections": [
+    {
+      "anchor": "rag-arch-overview",
+      "heading": "架构总览",
+      "level": 2,
+      "startLine": 3,
+      "endLine": 6,
+      "knowledgeHitCount": 2,
+      "content": "采用 Query Rewrite + Hybrid Retrieval + Cross-Encoder Rerank + Citation Guardrail。",
+      "relatedQuestions": [
+        {
+          "id": "demo-q-1",
+          "text": "为什么选择 RAG 而不是直接微调？",
+          "displayText": "为什么选择 RAG 而不是直接微调？",
+          "difficulty": "medium",
+          "score": 0.93,
+          "questionType": "system",
+          "translatedText": "为什么选择 RAG 而不是直接微调？",
+          "isRevisited": false,
+          "generatedCount": 1,
+          "generatedStatus": "ready",
+          "lastGeneratedAt": "2026-03-27T10:00:00.000Z",
+          "generated": {
+            "elevator_pitch": "知识更新快时优先 RAG。",
+            "full_answer_markdown": "RAG 在可追溯性、更新速度和成本上更适合业务知识域。",
+            "work_story": "RAGCompass 每日增量入库，不需要反复重训。",
+            "work_evidence_status": "direct",
+            "follow_ups": ["何时切微调？", "怎么控召回噪声？"],
+            "missing_basics": []
+          }
+        }
+      ]
+    },
+    {
+      "anchor": "retrieval-strategy",
+      "heading": "召回策略",
+      "level": 2,
+      "startLine": 8,
+      "endLine": 11,
+      "knowledgeHitCount": 3,
+      "content": "第一阶段向量召回 80 条 + BM25 40 条，融合去重后进入重排。",
+      "relatedQuestions": [
+        {
+          "id": "demo-q-4",
+          "text": "召回链路怎么兼顾延迟和覆盖率？",
+          "displayText": "召回链路怎么兼顾延迟和覆盖率？",
+          "difficulty": "hard",
+          "score": 0.95,
+          "questionType": "retrieval",
+          "translatedText": "召回链路怎么兼顾延迟和覆盖率？",
+          "isRevisited": false,
+          "generatedCount": 1,
+          "generatedStatus": "ready",
+          "lastGeneratedAt": "2026-03-27T10:06:00.000Z",
+          "generated": {
+            "elevator_pitch": "两阶段召回与动态 topk。",
+            "full_answer_markdown": "先扩召回保覆盖，再用重排和阈值裁剪控制时延。",
+            "work_story": "我们在 RAGCompass 对不同 query 长度动态调整 topk。",
+            "work_evidence_status": "direct",
+            "follow_ups": ["峰值时怎么退化？", "重排慢了怎么办？"],
+            "missing_basics": []
+          }
+        }
+      ]
+    },
+    {
+      "anchor": "chunking-strategy",
+      "heading": "Chunk 方案",
+      "level": 2,
+      "startLine": 13,
+      "endLine": 16,
+      "knowledgeHitCount": 2,
+      "content": "按语义段 + 结构标题双切片，窗口 420 token，重叠 80 token。",
+      "relatedQuestions": [
+        {
+          "id": "demo-q-5",
+          "text": "Chunk 策略怎么做，如何避免 lost-in-the-middle？",
+          "displayText": "Chunk 策略怎么做，如何避免 lost-in-the-middle？",
+          "difficulty": "medium",
+          "score": 0.91,
+          "questionType": "retrieval",
+          "translatedText": "Chunk 策略怎么做，如何避免 lost-in-the-middle？",
+          "isRevisited": false,
+          "generatedCount": 1,
+          "generatedStatus": "ready",
+          "lastGeneratedAt": "2026-03-27T10:08:00.000Z",
+          "generated": {
+            "elevator_pitch": "结构化切片 + 位置重排。",
+            "full_answer_markdown": "采用语义切片并给首尾片段加权，降低中段信息被忽视的问题。",
+            "work_story": "RAGCompass 在召回后会做位置校正重排。",
+            "work_evidence_status": "adjacent",
+            "follow_ups": ["重叠比例如何选？", "多语言文档怎么切？"],
+            "missing_basics": []
+          }
+        }
+      ]
+    }
+  ],
+  "looseRelatedQuestions": []
+}