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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

• 为什么单纯的向量检索不够，还要加一路关键词检索？Why isn't pure vector search enough — what does keyword search add?

  国内高频海外高频基础#rag#hybrid-search#retrieval

  分析过程 · 先想清楚再作答

  1. 这题的区分度不在「你知不知道有 hybrid search」，而在**你能不能说出一个向量检索一定会漏的具体例子**。答不出例子的，一听就是只看过架构图。
  2. 推导链只有一句：向量检索比的是语义距离，所以它的强项和弱项都来自「压缩成语义」这一步——同义词能对上（运费 / 邮费），而没有语义的字符串会被压到一起（E4032、SF-3000、订单号、人名）。
  3. 关键词那一路（BM25）的性质正好相反：一个词在本文档里越频繁越相关、在全语料里越常见越不值钱，所以它对低频稀有词极准，对同义改写完全无能。
  4. 结论要说成「两者的盲区不重叠，而且是由计算原理决定的不重叠」——不是「多一路更保险」这种模糊说法。举一个实测例子最有说服力：查「E4032 是什么意思」，向量 top5 里没有那篇讲支付错误码的文档，关键词 top1 就是它。
  5. 可预期的追问一：那怎么合并两路结果？答 RRF，并说清为什么不能加权求和（见 q02）。
  6. 可预期的追问二：中文怎么做关键词检索？答 Postgres 默认分词器对中文等于不分词，最简可用的兜底是 bigram（相邻两字切开），但英文与编号必须整词保留；生产要上专门的中文分词扩展。这一条能答出来，基本就说明你真动手做过。

  How to reason about it · think before answering

  1. The discriminator is not whether you know the term 'hybrid search' — it is whether you can name a concrete query that vector search will always miss. No example means you have only read architecture diagrams.
  2. One causal chain: vector search compares semantic distance, so both its strength and its weakness come from that compression step. Synonyms match (shipping fee vs postage), but strings with no semantics collapse together — error codes, SKUs, order ids, person names.
  3. BM25 has the mirror-image profile: a term matters more when it is frequent in this document and rare across the corpus. So it nails low-frequency literals and fails completely on paraphrase.
  4. State the conclusion as 'their blind spots do not overlap, and that follows from how each one computes' — not the vague 'two channels are safer'. A measured example lands best: for 'what does E4032 mean', the correct doc is absent from the vector top-5 and is the keyword top-1.
  5. Expected follow-up 1: how do you merge the two rankings? Answer RRF, and explain why weighted sums fail (see q02).
  6. Expected follow-up 2: how do you do keyword search over Chinese? Postgres's default parser effectively does not tokenize Chinese; the cheapest workable fallback is character bigrams, keeping ASCII words and codes whole. Production needs a real Chinese tokenizer extension. Answering this usually proves you actually built it.

  答题要点

  • 向量检索比的是语义距离，强在同义改写，弱在型号、错误码、订单号这类没有语义的字符串。
  • BM25 强在低频稀有词的字面命中，弱在同义改写——两者的盲区由各自的计算原理决定，不重叠。
  • 所以第一轮开两路、各取 20 条，用 RRF 融合，把两边的盲区互相补上。
  • 举实测例子：E4032 那条 query 向量 top5 漏掉正确文档，关键词 top1 就是它；「邮费」那条反过来只有向量能召回。
  • 中文关键词那一路要处理分词，最简兜底是 bigram，字母数字整词保留，生产上专门的中文分词扩展。

  Key points

  • Vector search compares semantic distance: strong on paraphrase, weak on SKUs, error codes and order ids that carry no semantics.
  • BM25 is strong on rare literal terms and weak on paraphrase — the blind spots follow from the algorithms and do not overlap.
  • So run both channels wide (top 20 each) and fuse with RRF so each covers the other's gap.
  • Give a measured example: for the E4032 query the correct chunk is missing from vector top-5 but is keyword top-1; a 'postage vs shipping fee' query is the reverse.
  • Chinese keyword search needs tokenization: character bigrams as the cheap fallback, ASCII words kept whole, a real tokenizer extension in production.