面试题库

30 天从前端工程师到 Agent 工程师

D24 RAG 进阶：hybrid search、rerank、引用、recall 评估

• 重排（rerank）一般怎么实现？它解决了初步检索的什么问题，代价是什么？How is reranking usually implemented, what problem does it solve, and what does it cost?

  国内高频海外高频进阶#rag#rerank#latency

  分析过程 · 先想清楚再作答

  1. 这题最容易答成「再排一次序，更准」。面试官想听的是**为什么第一轮不能直接排准**，以及**为什么重排不能对全库做**。
  2. 怎么拆：第一轮的排序依据是「检索信号」——余弦距离或词频统计，它们是为了能在百万条里快速筛选而设计的，代价就是粗。重排换了一种算法：把 query 和候选**拼在一起**送进同一个模型算相关度（cross-encoder），精度高得多，但复杂度是每条候选一次前向，没法对全库做。所以它必须跟在一个宽召回后面。
  3. 结论要区分两种实现：教学 / 原型可以用 LLM 批量打分（一次调用给 40 条打 0 到 10 分），生产用专门训练的 cross-encoder 重排模型。**代价说清楚：多一次 100 到 300 毫秒的调用，外加一台推理机器**——它不是按 token 计费的 API，是要占资源的。
  4. 把它放进漏斗里说最清楚：召回决定天花板，重排决定天花板上的东西能不能排到前五。实测的样子是——加了关键词那一路，recall@20 从 83% 涨到 95%（天花板抬高）；再加重排，recall@20 只到 98%，但 recall@5 从 80% 跳到 91%、MRR 从 0.732 到 0.908。
  5. 可预期的追问一：重排能不能提高召回？不能。它不引入新候选，只重排已有的那批——所以看 recall@20 判断重排效果是错的指标。
  6. 可预期的追问二：为什么不用 LLM 打分上生产？延迟不可控、成本按 token 走、分数会随提示词措辞漂移，而且没法做批量离线蒸馏。

  How to reason about it · think before answering

  1. The lazy answer is 'sort again, more accurately'. What the interviewer wants is why the first pass cannot rank well, and why reranking cannot run over the whole corpus.
  2. Decompose: the first pass ranks by retrieval signals — cosine distance or term statistics — which are designed to scan millions of items fast, and coarseness is the price. Reranking changes the algorithm: query and candidate go into one model together (a cross-encoder), which is far more accurate but costs one forward pass per candidate. Hence it must sit behind a wide recall stage.
  3. Distinguish two implementations. For teaching or prototypes, batch-score with an LLM (0-10 for 40 candidates in one call). Production uses a trained cross-encoder reranker. Name the cost: an extra 100-300 ms hop plus an inference box — it is not a per-token API, it consumes capacity.
  4. Framing it as a funnel is clearest: recall sets the ceiling, reranking decides whether what is under the ceiling reaches the top five. Measured: adding the keyword channel lifts recall@20 from 83% to 95%; adding reranking moves recall@20 only to 98%, but recall@5 jumps from 80% to 91% and MRR from 0.732 to 0.908.
  5. Expected follow-up 1: does reranking improve recall? No. It introduces no new candidates, so recall@20 is the wrong metric to judge it by.
  6. Expected follow-up 2: why not ship LLM scoring to production? Unpredictable latency, per-token cost, scores that drift with prompt wording, and no clean path to offline distillation.

  答题要点

  • 第一轮按检索信号粗排（余弦、词频），为的是能在大库里快速筛，代价是粗。
  • 重排把 query 和候选拼在一起过同一个模型（cross-encoder），精度高但每条一次前向，只能对几十条做。
  • 教学版可用 LLM 批量打 0 到 10 分；生产用专用重排模型，代价是多一次 100 到 300 毫秒的调用加一台推理机器。
  • 重排不提高召回，它提高的是 recall@5 与 MRR——实测 80% → 91%、0.732 → 0.908，而 recall@20 只从 95% 到 98%。
  • 所以判断重排效果要看前 k 小的指标，不要看 recall@20。

  Key points

  • The first pass ranks by retrieval signals so it can scan a large index fast; coarseness is the trade.
  • Reranking feeds query and candidate through one model together (cross-encoder): much sharper, but one forward pass per candidate, so only tens of items.
  • Batch LLM scoring works for teaching; production uses a dedicated reranker, costing an extra 100-300 ms hop plus an inference box.
  • Reranking does not raise recall — it raises recall@5 and MRR (measured 80% to 91%, 0.732 to 0.908) while recall@20 barely moves from 95% to 98%.
  • So judge a reranker by small-k metrics, never by recall@20.