面试题库

分析过程 · 先想清楚再作答

1. 这题是国内面试的极高频题，也是最容易暴露「只搭过没调过」的一题。判据很简单：你的回答里有没有出现**具体的指标名和标注粒度**，没有就是没做过。
2. 先把评估对象分清楚——这是最容易混的一步：**检索评估问「找得到找不到」，生成评估问「答得对不对」**。两套评估集要分开维护。混成一套的后果是分数掉了你分不清是检索漏了还是模型答砸了，而这两件事的修法完全不同。
3. 评估集的形状：20 条左右的 query，每条**人工标注 1 到 3 个必须召回的 chunkId**。注意标注粒度是**块**不是文档——检索的单位就是块，标到文档级会让指标虚高。query 要覆盖真实分布，尤其要包含那些你知道会翻车的类型（编号、同义改写、跨文档）。
4. 三个指标各回答一个问题：recall@5 是「进上下文的那几条覆盖了多少」，也就是你真正关心的数；recall@20 是天花板，它上不去说明问题在召回侧、重排再强也没用；MRR 对排序质量敏感，recall 打平时用它分高下。
5. 生产视角：评估集一旦定下来就要冻结，换了样本分数就没有可比性——这和产线质检必须用固定的标准样品是同一个道理。同时线上要有对照指标（引用为空率、幻觉引用率、转人工率），因为离线过了不等于线上没事。
6. 可预期的追问：标注成本这么高，20 条够吗？答：20 条不够做统计显著性，但足够做**回归**——它的作用是「改了检索之后别悄悄变差」。要做 A/B 定论再上规模，而且优先扩充失败案例，不是随机加样本。

How to reason about it · think before answering

1. This is a very common question in the Chinese market and the fastest way to expose someone who has assembled RAG but never tuned it. The test: does your answer contain concrete metric names and an annotation granularity?
2. First separate what is being evaluated — the step people most often conflate. Retrieval evaluation asks 'was it found'; generation evaluation asks 'was the answer right'. Keep two separate sets. Merge them and, when the score drops, you cannot tell whether retrieval missed or the model fumbled — and those have completely different fixes.
3. Shape of the set: about 20 queries, each annotated with 1-3 chunk ids that must be retrieved. Annotate at chunk level, not document level — chunks are the retrieval unit, and document-level labels inflate the numbers. Cover the real query mix, especially the types you know break: codes, paraphrase, cross-document.
4. Three metrics, three questions. recall@5 is what actually reaches the model, so it is the number you care about. recall@20 is the ceiling — if it does not move, the problem is on the recall side and no reranker will save you. MRR is sensitive to ordering and breaks ties when recall is equal.
5. Production view: freeze the set once agreed, because changing samples destroys comparability — the same reason a factory keeps fixed reference samples. Pair it with online counterparts (empty-citation rate, hallucinated-citation rate, escalation rate), since passing offline does not mean passing in production.
6. Expected follow-up: is 20 enough given the labelling cost? Not for statistical significance, but enough for regression — its job is to stop retrieval silently getting worse. Scale up before you settle an A/B, and grow it from failure cases rather than random additions.

分析过程 · 先想清楚再作答

1. 这题是本天的核心，也是最能筛掉「只跑过 demo」的人的一题。题眼在「漏」这个字：能不能说清楚漏的是条数还是排序，直接决定你被归到哪一档。
2. 先讲机制，一句话就够：近似索引的过滤发生在索引扫描之后。索引先按距离取回 ef_search 个候选，然后才拿 WHERE 去筛这一批。条件命中率越低，活下来的越少——命中 1% 的条件配默认的 40 个候选，平均只剩零点几条。
3. 然后把漏召回拆成两类，这是拿分点：一类是**结果条数不够**，十条只给了一两条；另一类是**条数够但排序不对**，十条都在只是排错了。两类的修法完全不同，混为一谈说明没真跑过。
4. 修法一是迭代扫描（pgvector 0.8.0 起）：候选被过滤掉太多时自动回索引里继续扫，直到凑够。它只解决第一类。两种模式的取舍要说清楚——严格顺序保证结果按距离排好，宽松顺序允许略微乱序换更高召回，代价都是延迟明显上升。
5. 修法二是预过滤，即让过滤条件先生效：条件很挑剔时给过滤列建普通索引走精确检索，取值只有少数几个时建部分索引，取值很多时按值做列表分区。代价分别是失去近似索引的加速、索引数量随取值爆炸、以及 DDL 与运维复杂度上升。
6. 可预期的追问：怎么判断该用哪一种？给一条可执行的判据——先看返回条数够不够。不够是第一类，先试迭代扫描；够了但召回低是第二类，只能加大 probes 或 ef_search，或者干脆改成预过滤。

How to reason about it · think before answering

1. This is the question that separates people who ran a demo from people who ran this in production. The tell is whether you distinguish missing rows from mis-ordered rows.
2. State the mechanism in one sentence: with approximate indexes, filtering is applied after the index scan. The index first collects ef_search candidates by distance, and only then applies the WHERE clause to that batch.
3. Do the arithmetic out loud: a condition matching 1% of rows against a default candidate list of 40 leaves well under one row on average. That is why the query looks broken even though the rows exist.
4. Split the failure into two kinds. Too few rows returned is one; enough rows but the wrong ones ranked first is the other. They have different fixes, and conflating them signals inexperience.
5. Fix one is iterative scanning, available since pgvector 0.8.0: when too many candidates are filtered out, keep scanning more of the index until enough results are found. Strict ordering keeps exact distance order, relaxed ordering trades slight reordering for better recall, and both cost latency.
6. Fix two is making the filter apply first: a plain index on the filter column for highly selective conditions, a partial index when there are only a few distinct values, list partitioning when there are many. The costs are losing the approximate speedup, index count exploding per value, and DDL plus operational complexity.
7. Expected follow-up: how do you pick? Check the returned row count first. Too few means iterative scanning; enough rows with low recall means raising probes or ef_search, or switching to pre-filtering.