docs(interview): refresh materials for single-agent harness narrative
Archive pre-refactor interview notes and add current deep-dives on architecture evolution, issue-derived stories, and evidence gates.
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# RAG 检索质量报告
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## 1. 目的
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这份报告回答一个面试关键问题:
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```text
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迁移到 Spring AI VectorStore 后,怎么证明检索质量没有退化?
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```
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这不是完整 benchmark,而是针对当前 Milvus/Zilliz collection 的代表性 live smoke comparison。
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## 2. 验证设置
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服务端点:
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```text
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GET http://127.0.0.1:9900/api/search/similar
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```
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collection:
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```text
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biz
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```
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对比模式:
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```text
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retrieval.vector-store.mode=sdk
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retrieval.vector-store.mode=spring-ai
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```
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每个 case:
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```text
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topK=3
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```
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## 3. 测试案例
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| Case | Query | 目的 |
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|---|---|---|
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| `err-timeout` | `ERR_TIMEOUT` | 精确错误码检索 |
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| `payment-service-timeout` | `payment-service timeout` | 服务超时排障 |
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| `mysql-connection-pool` | `MySQL connection pool is exhausted. How should I diagnose it?` | 数据库排障 |
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| `high-cpu-payment` | `HighCPUUsage payment-service` | AIOps 告警式检索 |
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| `rag-l0-l1` | `Should L0 keyword matching decide the final retrieval result?` | 抽象 RAG 设计问题 |
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| `database-filter` | `mysql timeout`, category=`database` | metadata filter 行为 |
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## 4. 对比摘要
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| Case | SDK 数量 | VectorStore 数量 | Top1 一致 | TopK 重叠 | 结论 |
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|---|---:|---:|---|---:|---|
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| `err-timeout` | 3 | 3 | 是 | 3/3 | 文档和顺序一致 |
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| `payment-service-timeout` | 3 | 3 | 是 | 3/3 | 文档和顺序一致 |
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| `mysql-connection-pool` | 3 | 3 | 是 | 3/3 | 文档和顺序一致 |
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| `high-cpu-payment` | 3 | 3 | 是 | 3/3 | AIOps 核心 query 一致 |
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| `rag-l0-l1` | 3 | 1 | 是 | 1/3 | VectorStore 尾部结果更少 |
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| `database-filter` | 0 | 0 | 不适用 | 不适用 | filter 行为一致,taxonomy 有问题 |
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## 5. 代表性结果
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### ERR_TIMEOUT
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SDK:
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```text
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1. ERR_TIMEOUT score=0.5659486 label=l2_distance
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2. ERR_GATEWAY_TIMEOUT score=0.6048740 label=l2_distance
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3. Error handling score=0.7735061 label=l2_distance
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```
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VectorStore:
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```text
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1. ERR_TIMEOUT score=0.5659486 rawScore=0.4340513 label=similarity
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2. ERR_GATEWAY_TIMEOUT score=0.6048740 rawScore=0.3951259 label=similarity
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3. Error handling score=0.7735061 rawScore=0.2264938 label=similarity
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```
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解释:
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- 排序一致。
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- 兼容 `score` 与 SDK L2 distance 一致。
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- `rawScore` 暴露 Spring AI similarity。
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### MySQL connection pool
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两条路径都返回:
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```text
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1. MySQL connection pool config
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2. wait_timeout timeout
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3. idle-timeout
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```
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说明迁移保留了核心基础设施排障检索能力。
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### HighCPUUsage payment-service
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两条路径都返回 payment-service 高 CPU 相关排障文档,说明 AIOps 告警式 query 没有退化。
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### rag-l0-l1
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VectorStore 只返回一个候选,但 Top1 与 SDK 一致。这说明抽象设计类 query 需要后续 query rewrite、补充索引或 threshold 调整。
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### database-filter
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两条路径都返回 0,因为相关 MySQL 文档当前分类是 `infrastructure`,不是 `database`。这是 metadata taxonomy 问题,不是 VectorStore 回归。
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## 6. 分数兼容结论
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对比验证了当前分数设计:
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```text
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SDK:
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score = L2 distance
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rawScore = L2 distance
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scoreLabel = l2_distance
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VectorStore:
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score = Milvus metadata.distance
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rawScore = Spring AI similarity
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scoreLabel = similarity
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```
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这样既保持 `lookup_knowledge` 原有归一化逻辑,又能暴露 VectorStore 语义。
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## 7. 验收结论
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Spring AI VectorStore 读路径可以接受用于当前 MVP/面试:
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- 核心排障和 AIOps case 与 SDK top3 一致。
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- 分数兼容性保留。
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- VectorStore 语义通过 `rawScore` 和 `scoreLabel` 可观察。
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- SDK fallback 仍保留运行安全。
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后续检索质量工作不阻塞这次迁移,应作为独立优化继续推进。
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## 8. 下一步
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- 增加自动 live comparison 脚本。
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- 在 offline evaluator 中加入 topK overlap、top1 hit、MRR。
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- 规范 metadata category,例如 `database` 与 `infrastructure`。
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- 为抽象设计类 query 增加 query rewriting。
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- 后续再评估是否迁移写入路径到 `VectorStore.add(...)`。
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